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The property/liability insurance cycle: A comparison of alternative models.



Seungmook Choi Choi may refer to:
  • Choi, a Cantonese romanisation of Cai, a Chinese surname. (See Transliteration and romanization of Cai)
  • Choi, a Korean surname.
  • CHOI-FM, a radio station in Quebec City, Canada.
 (*)

Don Hardigree (*)

Paul Paul, 1901–64, king of the Hellenes (1947–64), brother and successor of George II. He married (1938) Princess Frederika of Brunswick. During Paul's reign Greece followed a pro-Western policy, and the Cyprus question was temporarily resolved.  D. Thistle thistle, popular name for many spiny and usually weedy plants, but especially applied to members of the family Asteraceae (aster family) that have spiny leaves and often showy heads of purple, rose, white, or yellow flowers followed by thistledown seeds (a favorite  (+)

The objective of this paper is to compare alternative models of insurance pricing as theories of the property-liability underwriting Underwriting

1. The process by which investment bankers raise investment capital from investors on behalf of corporations and governments that are issuing securities (both equity and debt).

2. The process of issuing insurance policies.
 cycle. The existing literature has focused on comparing two models, the financial pricing and capacity constraint Constraint

A restriction on the natural degrees of freedom of a system. If n and m are the numbers of the natural and actual degrees of freedom, the difference n - m is the number of constraints.
 models. However, these are not the only relevant models. We show that six alternative models imply the same general form of the pricing equation. We apply the model to data on stock property-liability insurers for the period 1935-1997. We find that the actuarial ac·tu·ar·y  
n. pl. ac·tu·ar·ies
A statistician who computes insurance risks and premiums.



[Latin
 model and the capacity constraint hypothesis are the only theoretical models that are consistent with the data.

1. Introduction

Property/liability insurance markets alternate between hard and soft markets in a phenomenon known as the underwriting cycle. In soft markets, underwriting standards are relaxed, prices and profits are low, and the quantity of insurance increases. In hard markets, underwriting standards become restrictive, and prices and profits increase. There are many policy cancellations or non-renewals, and policy terms (deductibles and policy limits) are tightened as the quantity of insurance coverage generally decreases. (1) The increases in insurance premiums and decreases in the availability of insurance can be sufficiently sudden and severe that hard markets are sometimes referred to as "liability crises." Between 1984 and 1986, industry premium revenue nearly tripled, and availability problems were widely discussed in the popular press, especially in general and municipal liability and in medical malpractice Improper, unskilled, or negligent treatment of a patient by a physician, dentist, nurse, pharmacist, or other health care professional. . Following a hard market, prices and profits remain high, then gradually erode Erode (ĕrōd`), city (1991 urban agglomeration pop. 361,755), Tamil Nadu state, S India, on the Kaveri River. The city is located in a cotton-growing region, and its industries include cotton ginning and the manufacture of transport equipment.  as the market softens. The mos t recent hard markets were during 1975-1977 and 1984-1987. The consensus estimate is that the cycle has a period of about six to eight years. On this view, at least one hard market should have occurred in the early to mid- mid-
pref.
Middle: midbrain. 
1990s and possibly another in the late 1990s. However, market conditions have remained soft since the late 1980s. Why the underwriting cycle occurs and whether the cycle continues to characterize insurance markets are questions that do not have complete answers.

Both soft and hard markets can create problems. Soft markets may contribute to insolvencies if insurers price and underwrite To insure; to sell an issue of stocks and bonds or to guarantee the purchase of unsold stocks and bonds after a public issue.

The word underwrite has two meanings.
 too aggressively, while high prices and restrictive underwriting in hard markets may disrupt the flow of goods and services In economics, economic output is divided into physical goods and intangible services. Consumption of goods and services is assumed to produce utility (unless the "good" is a "bad"). It is often used when referring to a Goods and Services Tax. . Since the insurance industry is subject to regulation at the state level, understanding the behavior of property/liability insurance markets is important for the development of appropriate public policies.

Over the last decade, a substantial body of insurance literature has developed attempting to explain the recurrence recurrence /re·cur·rence/ (-ker´ens) the return of symptoms after a remission.recur´rent

re·cur·rence
n.
1.
 of hard markets in property/liability insurance. Most recent studies view the recurrent recurrent /re·cur·rent/ (re-kur´ent) [L. recurrens returning]
1. running back, or toward the source.

2. returning after remissions.


re·cur·rent
adj.
1.
 hard markets as the result of an equilibrium equilibrium, state of balance. When a body or a system is in equilibrium, there is no net tendency to change. In mechanics, equilibrium has to do with the forces acting on a body.  or market-clearing process. The existing literature has focused on comparing two models of insurance pricing, the financial pricing/rational expectations model and the capacity constraint model, as a possible explanation of underwriting cycles. However, since any model of insurance pricing has implications for the underwriting cycle, these are not the only relevant models. Some models (e.g., the actuarial model) are not specifically intended as models of the underwriting cycle, while others (e.g., the capacity constraint model) are. But any model of insurance prices implies that the price depends only on certain variables. Consequently, each model implies that cycles in premiums and profits depend only on those same variables. The objective of this paper is to comp comp

See comparison.
 are the alternative insurance pricing models as theories of the property-liability underwriting cycle.

Financial models of insurance pricing based on discounted cash flows and the capital asset pricing model Capital asset pricing model (CAPM)

An economic theory that describes the relationship between risk and expected return, and serves as a model for the pricing of risky securities.
 imply that prices equal policy expenses plus the expected present value of claims (e.g., Myers Myers can refer to: People
  • Myers, Alan, U.S. drummer (Devo)
  • Myers, Alan, translator
  • Myers, Amanda (born 1984) Green Party Candidate, Canadian
  • Myers, B. R, critic (“A Reader's Manifesto”)
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 and Cohn 1987). Cummins This article is about the diesel engine manufacturer. For other uses, see Cummins (disambiguation).
Cummins Inc. (NYSE: CMI) is a maker of diesel and natural gas engines whose corporate headquarters is located in Columbus, Indiana.
 and Outreville (1987) assume that insurers have rational expectations regarding claims costs and argue that contracting and informational features of the market, regulatory lags, and financial reporting procedures create an apparent insurance cycle (see also Doherty
for people named Doherty see: Doherty (disambiguation)
The Doherty Clan (Irish: Clann Ua Dochartaigh) is an Irish clan based in County Donegal in the north of the island of Ireland.
 and Kang KANG Kansas Air National Guard  1988). These models implicitly assume that capital markets adjust quickly. The capacity constraint model (Winter 1988, 199 la, 1994; Doherty and Posey A posey can be a flower bouquet. As a surname it is of French and English origins, originating and or derived from the greek word Desposyni. People whose surname is or was Posey include:
  • John Posey -an actor
  • Buford Posey - Civil rights worker
  • Francis B.
 1993; Gron 1994a, b, 1995; Doherty and Garven 1995) is based on the assumption of capital market imperfections. The slow adjustment of capital implies that both hard and soft markets will persist. (2) These authors provide empirical evidence that is consistent with the capacity constraint hypothesis. (3) The financial quality hypothesis (Harrington Harrington can refer to:

Places in the United Kingdom:
  • Harrington, Cumbria
  • Harrington, Lincolnshire
  • Harrington, Northamptonshire
Places in the United States:
  • Harrington, Delaware
  • Harrington, Maine
  • Harrington, Washington
 and Danzon 1994; Cagle and Harrington 1995; Cummins and Danzon 1997) extends the capacity constraint model by allowing insurers' default risk to be endogenous endogenous /en·dog·e·nous/ (en-doj´e-nus) produced within or caused by factors within the organism.

en·dog·e·nous
adj.
1. Originating or produced within an organism, tissue, or cell.
. The contingent claims Contingent claim

A claim that can be made only if one or more specified outcomes occur.
 analysis or option pricing (OP) model is a different financial approach to insurance pricing in which the insurance policy is viewed as analogous analogous /anal·o·gous/ (ah-nal´ah-gus) resembling or similar in some respects, as in function or appearance, but not in origin or development.

a·nal·o·gous
adj.
 to risky debt; this model has different implications for the underwriting cycle. (4)

There are also nonfinancial Adj. 1. nonfinancial - not involving financial matters
financial, fiscal - involving financial matters; "fiscal responsibility"
 models of insurance pricing. In practice, actuarial models are widely used in insurance pricing. In the basic actuarial model, the price of a policy is equal to the expected cost of the policy (expected claims costs plus policy expenses) plus a "risk loading" or "buffer buffer, solution that can keep its relative acidity or alkalinity constant, i.e., keep its pH constant, despite the addition of strong acids or strong bases.  fund" chosen to hold the probability of default Probability of default (PD) is a parameter used in the calculation of economic capital or regulatory capital under Basel II for a banking institution. This is an attribute of bank's client.  to a predetermined pre·de·ter·mine  
v. pre·de·ter·mined, pre·de·ter·min·ing, pre·de·ter·mines

v.tr.
1. To determine, decide, or establish in advance:
 level. The well-known well-known
adj.
1. Widely known; familiar or famous: a well-known performer.

2. Fully known: well-known facts.
 Sandmo (1971) and Leland Leland is the name of several places:
  • Leland, Illinois
  • Leland, Iowa
  • Leland, Michigan
  • Leland, Mississippi
  • Leland, North Carolina
  • Leland, Utah
  • Leland Grove, Illinois
  • Leland, Norway
There's also:
  • Leland River
 (1972) model of the competitive firm under uncertainty can also be applied to the property/liability insurance market. The Sandmo-Leland model is based on the assumption that the firm acts as if it is a risk-averse Risk-averse

Describes an investor who, when faced with two investments with the same expected return but different risks, prefers the one with the lower risk.
 expected utility maximizer and implies that the price of a policy equals the expected cost of the policy plus a risk premium.

The objective of the paper is to compare these six alternative insurance pricing models and determine which of the models is supported by the data. We show that these models imply the same general form for the pricing equation. However, they have different implications for the determinants of prices in the short run and long run. Both short- and long-run adj. 1. relating to or extending over a relatively long time; as, the long-run significance of the elections s>.

Adj. 1. long-run
 analyses are necessary to distinguish between the models. Special care is required in the development of the empirical model since some of the variables in question are stationary Stationary can mean:
  • Fixed in position, or mode: immobile.
  • Unchanging in condition or character.
  • In statistics and probability: a stationary process.
  • In mathematics: a stationary point.
  • In mathematics: a stationary set.
 while others are nonstationary. We examine the possible cointegrating relationships among insurance prices, interest rates, and surplus. Taking the long-run relationships into account, we then examine the short-run Adj. 1. short-run - relating to or extending over a limited period; "short-run planning"; "a short-term lease"; "short-term credit"
short-term

short - primarily temporal sense; indicating or being or seeming to be limited in duration; "a short life"; "a
 dynamics of insurance prices. Several of the models imply that, in the short run, insurance prices depend on the interest rate and a risk premium. The risk premium in turn depends on surplus and the conditional variance In statistics, conditional variance is a special form of the variance. If we have a conditional distribution Y|X the conditional variance is defined as



where
 of the loss distribution. Various researchers have p rovided empirical evidence in support of the financial pricing and capacity constraint models as alternative models of the underwriting cycle. This paper considers a wider range of models and provides the first direct empirical comparison of these models.

This paper is organized as follows. In the next section, we discuss the alternative models. In section 3, we develop the empirical model and present the empirical results. Our conclusions are offered in section 4.

2. Alternative Models of Insurance Pricing

In this section, we provide brief descriptions of the alternative models of insurance pricing. We focus on the main implications of the models for long-run equilibrium relationships and for the short-run dynamics of prices.

We assume that there is a representative firm that operates in a single period. At the beginning of the period, the firm issues q insurance policies, which are sold at price P. The firm pays policy expenses (e.g., commissions, marketing expenses) of c per policy. The firm may invest beginning-of-period resources at the interest rate r. During the period, policyholders realize losses and file claims with the insurance company. Letting x denote de·note  
tr.v. de·not·ed, de·not·ing, de·notes
1. To mark; indicate: a frown that denoted increasing impatience.

2.
 the average claim per policy, policyholders' total claims are xq and are paid at the end of the period. The average claim per policy has mean [micro] and variance The discrepancy between what a party to a lawsuit alleges will be proved in pleadings and what the party actually proves at trial.

In Zoning law, an official permit to use property in a manner that departs from the way in which other property in the same locality
 [sigma] (2.5) We assume throughout that insurers have rational expectations with respect to losses. Letting p = P - c denote price net of expenses, the present value of underwriting profit Underwriting profit is a term used in the insurance industry. It consists of the earned premium remaining after losses have been paid and administrative expenses have been deducted. It does not include any investment income earned on held premiums.  is

[pi](q) = pq - xq/(l + r). (1)

The underwriting profit is random since the average claim per policy is random.

The financial pricing model is based on the assumptions that the firm is risk neutral and has rational expectations with respect to losses. It also assumes that markets are perfect; in particular, financial capital adjusts quickly and at zero cost. Then the premium net of expenses is equal to the expected present value of losses,

p = [micro]/(1 + r). (2)

The price is a decreasing function of the interest rate and does not depend on any other variables. The model is illustrated in Figure 1, where the supply of insurance is perfectly elastic elastic

Of or relating to the demand for a good or service when the quantity purchased varies significantly in response to price changes in the good or service.
 in both the short and the long run.

The capacity constraint hypothesis (CCH CCH Colegio de Ciencias y Humanidades (Spanish)
CCH Certified Clinical Hypnotherapist
CCH Cook County Hospital
CCH Certified in Classical Homeopathy
CCH Country Club Hills (Fairfax City, VA, USA) 
) is a model of short-run price determination. As a consequence of limited liability, insurers must hold equity to maintain the ability to pay claims, to keep the probability of bankruptcy bankruptcy, in law, settlement of the liabilities of a person or organization wholly or partially unable to meet financial obligations. The purposes are to distribute, through a court-appointed receiver, the bankrupt's assets equitably among creditors and, in most  low, and to meet regulatory requirements Regulatory requirements are part of the process of drug discovery and drug development. Regulatory requirements describe what is necessary for a new drug to be approved for marketing in any particular country. . The CCH assumes that there are costs to adjusting the firm's equity and that external equity is more costly than internal equity. (6) Firms hold excess capacity to reduce the probability of becoming capacity constrained con·strain  
tr.v. con·strained, con·strain·ing, con·strains
1. To compel by physical, moral, or circumstantial force; oblige: felt constrained to object. See Synonyms at force.

2.
 in the future, so that soft markets persist. In a hard market, the adjustment costs of raising new equity, combined with the expectation that the bard market will be temporary, implies that firms accumulate Accumulate

Broker/analyst recommendation that could mean slightly different things depending on the broker/analyst. In general, it means to increase the number of shares of a particular security over the near term, but not to liquidate other parts of the portfolio to buy a security
 surplus internally (Winter 199 la, pp. 126-7).

The capacity constraint model is also illustrated in Figure 1, where the short-run supply curve [S.sub.0] assumes a "normal" level of surplus. Given this level of surplus, the capacity constraint becomes binding at [q.sub.0]. As the capacity constraint is approached, the price of the policy must rise in order to maintain an acceptable probability of bankruptcy. At a sufficiently high price, premium revenue is always sufficient to cover claims. An increase in the degree of uncertainty increases the price of insurance in the short run, as higher prices are required to maintain the ability to pay claims (Winter 1988, p. 484). A negative shock to surplus shifts the short-run supply curve to [S.sub.1], and the price increases to [P.sub.1]. Firms earn positive profits at [P.sub.1], so the capacity constraint can be binding temporarily. As surplus accumulates over time, the market returns to long-run equilibrium. The capacity constraint model implies, in the short run, that prices are decreasing in the interest rate and surplus and increasing in the variance. The long-run supply curve is [S.sub.LR]; that is, the financial pricing model holds in the long run.

The CCH implies that the price of insurance can be written as

p = [micro]/(1 + r) + R, (3)

where R(S, [[sigma].sup.2]) is the short-run deviation DEVIATION, insurance, contracts. A voluntary departure, without necessity, or any reasonable cause, from the regular and usual course of the voyage insured.
     2.
 from the long-run equilibrium. Thus, there is a level of surplus, [S.sup.*], above which R = 0 and the price of a policy is just equal to the expected present value of claims. In terms of Figure 1, [S.sup.*] is the level of surplus that leads to the capacity constraint at [q.sub.0]. If surplus falls below this level, then the capacity constraint is binding, and the price rises; that is, if S < [S.sup.*], then R > 0. In the long run, R = 0.

The financial quality hypothesis (FQH FQH Fuzed Quartz Helix ) assumes that the firm is risk neutral but faces a solvency The ability of an individual to pay his or her debts as they mature in the normal and ordinary course of business, or the financial condition of owning property of sufficient value to discharge all of one's debts.


solvency n.
 constraint that premium revenues plus surplus must be sufficient to pay claims:

[x.sup.*] = (1 + r)(p + S/q) [greater than or equal to] x. (4)

In the FQH, the degree of insolvency risk Insolvency risk

The risk that a firm will be unable to satisfy its debts. Also known as bankruptcy risk.
 is endogenous and large enough to affect prices. The firm's objective is to maximize expected profit conditional on solvency, E{[pi](q) + S + K\x [less than or equal to][x.sup.*]} - (1 + r)S, where K denotes bankruptcy costs (Harrington and Danzon 1994; Cagle and Harrington 1995). (7) The FQH also assumes that the demand for insurance depends on the firm's financial quality or default risk. (8) This implies that shocks to surplus shift both demand and supply in the short run. The effect of the supply shift is greater than the effect of the demand shift, and a negative shock to surplus leads to an increase in price in the short run (Cagle and Harrington 1995, p. 227). The financial quality model also implies that price depends on the riskiness of the loss distribution, but the effect is ambiguous. (9)

The financial quality model implies that the firm has an optimal capital structure. Higher levels of surplus imply higher levels of financial quality and a greater willingness to pay Willingness to pay (WTP) generally refers to the value of a good to a person as what they are willing to pay, sacrifice or exchange for it. See also
  • Becker-DeGroot-Marschak method
 on the part of consumers. The firm equates the marginal revenue Marginal revenue

The change in total revenue as a result of producing one additional unit of output.


marginal revenue

The extra revenue generated by selling one additional unit of a good or service.
 from being able to charge higher prices to the marginal cost Marginal cost

The increase or decrease in a firm's total cost of production as a result of changing production by one unit.


marginal cost

The additional cost needed to produce or purchase one more unit of a good or service.
 of holding additional surplus. Then firms with higher levels of surplus command higher prices. This implies that prices and surplus are positively correlated cor·re·late  
v. cor·re·lat·ed, cor·re·lat·ing, cor·re·lates

v.tr.
1. To put or bring into causal, complementary, parallel, or reciprocal relation.

2.
 in the long run. (10) In the FQH prices are given by Equation 3, but now R has two components. The first component, [R.sub.s] is the short-run disequilibrium disequilibrium /dis·equi·lib·ri·um/ (dis-e?kwi-lib´re-um) dysequilibrium.

linkage disequilibrium
 component of prices. The second component, [R.sub.L], is the premium for financial quality and is a long-run component of the price.

In practice, insurance rates are based on actuarial principles. The primary concern in actuarial pricing models is with setting prices to achieve a predetermined ruin probability or probability of insolvency insolvency

Condition in which liabilities exceed assets so that creditors cannot be paid. It is a financial condition that often precedes bankruptcy. In the context of equity, insolvency is the inability to pay debts as they become due; insolvency under the balance-sheet
. Actuarial pricing models imply a risk premium arising from the need for a buffer fund to achieve an acceptable ruin probability. To see this, suppose that q is large enough that the central limit theorem central limit theorem

In statistics, any of several fundamental theorems in probability. Originally known as the law of errors, in its classic form it states that the sum of a set of independent random variables will approach a normal distribution regardless of the
 applies, and let [Z.[alpha]] be the upper [alpha] point of the standard normal distribution. Then the premium is again given by Equation 3, but where R = [Z.[alpha]] [sigma] - S/q is now the buffer fund per policy required to yield a ruin probability of [alpha]. (11) The actuarial model implies that underwriting profits depend positively on the variance of losses and negatively on surplus. The actuarial model is similar to the financial pricing model in that supply is perfectly elastic in the short run, albeit at a higher price. The actuarial model also implies that policyholders bear the cost of bankruptcy risk Bankruptcy Risk

The risk that a company will be unable to meet its debt obligations. Often referred to as "default" or "insolvency risk".

Notes:
This is a risk that both equity- and bondholders take when deciding to invest in a company.
 through the higher price. If the risk premium is positive, that is, if [Z.[alpha]] [sigma] > S/q, then the firm will, at least on average, accumulate surplus. In addition, we would expect that as the result of the normal operation of capital markets, firms would attract capital as long as expected profits are positive. The implication is that in long-run equilibrium, the risk premium will be driven to zero, and the premium will equal the expected present value of losses.

The model can be contrasted to the OP or contingent claims analysis approach to insurance pricing. The OP approach is based on the recognition that, since insurers have the option to default, an equity position in the insurer An individual or company who, through a contractual agreement, undertakes to compensate specified losses, liability, or damages incurred by another individual.

An insurer is frequently an insurance company and is also known as an underwriter.
 can be characterized char·ac·ter·ize  
tr.v. character·ized, character·iz·ing, character·iz·es
1. To describe the qualities or peculiarities of: characterized the warden as ruthless.

2.
 as a call option on the assets of the firm. Letting C denote the value of the call option, this implies that C(S + pq, r, [[sigma].sup.2]) = S; this must hold in both the short and the long run. The insurer's option to default implies that insurance policies have the characteristics of risky debt. Accordingly, even in the simplest setting, premiums are equal to policy expense plus the expected present value of claims less an adjustment for default risk. That is, policyholders hold a short position in a put option on the assets of the insurer with an exercise price equal to aggregate losses. The value of this put option is B(r, S, [[sigma].sup.2]). Premiums are once again given by Equation 3, where now R = -B/q and where B is the value of the insolvency or bankruptcy put option. (12) In the OP approach, it is policyholders rather than the firm that is compensated for risk bearing. The value of the bankruptcy put is a decreasing in the firm's surplus and increasing in the variance of losses. It follows that price is increasing in surplus and decreasing in the variance of losses; however, the effect of the interest rate is ambiguous. (13)

All the foregoing models assume that the firm is risk neutral. An alternative model is the well-known Sandmo (1971) and Leland (1972) model, which we refer to as the economic model. The firm is assumed to be risk averse Risk Averse

Describes an investor who, when faced with two investments with a similar expected return (but different risks), will prefer the one with the lower risk.

Notes:
A risk averse person dislikes risk.
 with utility function u, where u' > 0, u" < 0. (14) The firm's objective is to maximize the expected utility of terminal wealth, [max.sub.q] E{u[(1 + r)(S + [pi](q))]}. The first-order first-order - Not higher-order.  condition is

E{u'(.)[p - x/(1 + r)]} = 0. (5)

We assume that the maximization problem has a unique interior solution. Risk aversion risk aversion

The tendency of investors to avoid risky investments. Thus, if two investments offer the same expected yield but have different risk characteristics, investors will choose the one with the lowest variability in returns.
 implies that the firm must be compensated for bearing risk. That is, the price of the policy must strictly exceed the expected cost of the policy, including both policy expenses and the expected present value of claims, by an amount sufficient to compensate the firm for the risk of the policy. Then in the short run, price is again given by Equation 3; R is a strictly positive risk premium and is increasing in the degree of riskiness and decreasing in the amount of surplus. It can be shown that the model implies that prices and underwriting profits are decreasing in the interest rate.

In the long run, remaining in the market must yield at least as much expected utility as exiting. The participation constraint
  • In software engineering, Entity-relationship models have participation constraints.
  • In economics, participation constraints are a property of some mechanisms
 is

E{u[(1 + r)(S + [pi](q*))]} [greater than or equal to] u(S). (6)

This constraint holds as an equality in long-run equilibrium and can be interpreted as implicitly defining normal expected profits. If the firm is strictly risk averse, the participation constraint implies that expected profits are strictly positive. Since the firm is risk averse, normal expected profits must include compensation for the risk it bears due to the random nature of policyholders' claims. Thus, assuming that it is binding, the participation constraint implies that prices are again given by Equation 3, where the risk premium R depends on the interest rate, surplus, and the variance.

All the models imply the price of insurance can be written in the general form

p = [micro]/(1 + r) + R(r, S, [[sigma].sup.2]) (7)

in both the short and the long run. We carry Out the empirical analysis using the economic loss ratio (Winter 1994). The economic loss ratio is the ratio of an estimate of discounted losses to premiums net of expenses and is a reciprocal Bilateral; two-sided; mutual; interchanged.

Reciprocal obligations are duties owed by one individual to another and vice versa. A reciprocal contract is one in which the parties enter into mutual agreements.
 measure of price. For convenience, the empirical implications of these models for the economic loss ratio are summarized in Table 1. The "[+ or -]" indicates that a model implies that the variable is a determinant determinant, a polynomial expression that is inherent in the entries of a square matrix. The size n of the square matrix, as determined from the number of entries in any row or column, is called the order of the determinant.  of prices and profits but that the comparative statics Comparative statics is the comparison of two different equilibrium states, before and after a change in some underlying exogenous parameter. As a study of statics it compares two different unchanging points, after they have changed.  effect is ambiguous. A "0" indicates that the variable is not a determinant of prices and profits.

An important question is whether these models have different implications for the underwriting cycle; that is, are the models observationally equivalent? Two models are observationally equivalent if all cells in Table 1 are the same. Examining the short-run implications, the actuarial model, CCH, and economic model are all observationally equivalent, and the short-run implications of the FQH are similar. However, the long-mn implications of the economic model and the FQH are different from each other and from the actuarial and GCH GCH Gas Central Heating
GCH Gym Class Heroes (band)
GCH Grant Channel
GCH Grand Cross of Hanover (knight)
GCH Gas Collection Header
 models. The actuarial model and the CCH are observationally equivalent. No other two models are observationally equivalent. It is important to recognize that both the long- long-
Adverb

(in combination) for or lasting a long time: long-established, long-lasting 
 and the short-run implications of the models must be examined in order to distinguish among the models.

3. Empirical Analysis

In this section, we develop the empirical model based on the theoretical analysis of property liability insurers. Our objective is to construct an empirical model that is sufficiently general to encompass the alternative models of the insurance cycle. We then apply the model to aggregate data on stock property/liability insurers for the period 1935-1997.

In order to carry out the empirical analysis, we need data on the price of insurance, the interest rate, and measures of underwriting capacity or surplus. We measure the price of insurance using the economic loss ratio (ELR ELR Emergency Locking Retractor (seat belts)
ELR Environmental Law Reporter
ELR Everybody Loves Raymond (TV series)
ELR East Lancashire Railway (UK) 
), which is the ratio of an estimate of discounted losses to premiums net of expenses (Winter 1994). In principle, we need data on cash flows of claims paid to estimate discounted losses. (15) However, these data are not available for the full sample period. We follow the procedure in Winter (1994) to estimate this ratio from the available data on undiscounted losses. The ELR is estimated as ELR = D X LR/(1 - ER), where ER is the expense ratio, LR is the loss ratio, and D is the discount factor. The expense ratio is the ratio of expenses to premiums written, and the loss ratio is the ratio of calendar year losses to premiums earned. (16) In our notation notation: see arithmetic and musical notation.


How a system of numbers, phrases, words or quantities is written or expressed. Positional notation is the location and value of digits in a numbering system, such as the decimal or binary system.
, these measure cq/Pq and xq/Pq, so that LR/(1 - ER) measures xq/pq. To estimate discounted losses, let [[beta].sub.s] be the fraction of cl aims that are paid in the sth year after a policy is written. Assuming that [beta].sub.s] is constant over time, then D = [SIGMA] [beta].sub.s]/[(1 + r).sup.s] is an estimate of the present value of discounted claims as a fraction of premiums. The estimates of [[beta].sub.s] are taken from Winter (1994, p. 414) and assume that all claims are paid in eight years.(17) We use the three-month T-bill rate (TBR TBR Tennessee Board of Regents
TBR Technology Business Research
TBR To Be Read
TBR Travel Business Roundtable
TBR To Be Resolved
TBR To Be Reviewed
TBR Technical Basis for Regulation
TBR To Be Recorded
TBR Total Business Return
TBR To Be Revised
), the three- to five-year government note rate (MOB), and the long-term Long-term

Three or more years. In the context of accounting, more than 1 year.


long-term

1. Of or relating to a gain or loss in the value of a security that has been held over a specific length of time. Compare short-term.
 government bond rate (LOB) as discount rates to construct the variables ELR1, ELR2, and ELR3, respectively.

Three measures of surplus are used. The first is the ratio of policyholders' surplus to premiums written (BCAP BCAP Oncology A chemotherapy regimen consisting of BCNU, cyclophosphamide, Adriamycin-doxorubicin, prednisone ), the second is the ratio of surplus to assets (BSA 1. BSA - Business Software Alliance.
2. BSA - Bidouilleurs Sans Argent.
), and the third is a measure of relative capacity (RC). Relative capacity is measured as the ratio of surplus to the average surplus over the previous five years, [RC.sub.t] = [S.sub.t]/[[SIGMA].sup.5] [S.sub.t-j]. Winter (1994) interprets RC as the cyclical cyclical

Of or relating to a variable, such as housing starts, car sales, or the price of a certain stock, that is subject to regular or irregular up-and-down movements.
 component of surplus. For all three variables, surplus is measured as of the beginning of the year. The insurance industry data are taken from Best's Aggregates and Averages. The interest rate data are taken from the St. Louis Louis, titular duke of Burgundy
Louis, 1682–1712, titular duke of Burgundy; grandson of King Louis XIV of France. He became heir to the throne on the death (1711) of his father, Louis the Great Dauphin.
 Federal Reserve Bank's FRED database, the NBER NBER National Bureau of Economic Research (Cambridge, MA)
NBER Nittany and Bald Eagle Railroad Company
 Macrohistory database, and the Federal Reserve Bulletin.

Previous empirical work has found that prices follow an AR(2) process. An important question is why premiums are serially correlated. Suppose that the financial pricing/rational expectations model holds. Then prices are [p.sub.t] = E {x,\[I.sub.t]}/(1 + r), where [I.sub.t] is the information set at time t. If losses are i.i.d. from some distribution with mean [mirco Mirco Games was a pinball and arcade game manufacturer from Phoenix, Arizona that existed from 1969 to 1978. They were originally called Arizona Automation from 1969 to 1973 and then changed their name to Mirco in 1974 . ], prices are given by [p.sub.t] = [micro]/(1 + r), and the ELR should not be serially correlated. Suppose instead that losses follow some stationary AR process [x.sub.t] = [phi](L)[x.sub.t-1]) + [e.sub.t], where L is the lag operator In time series analysis, the lag operator or backshift operator operates on an element of a time series to produce the previous element. For example, given some time series

 and the [e.sub.t] are i.i.d. (0, [[sigma].sup.2.sub.e]. If the information set contains [x.sub.t-1], then [p.sub.t] = [phi](L)[x.sub.t-1]/(1+r). That is, the serial correlation serial correlation

The relationship that one event has to a series of past events. In technical analysis, serial correlation is used to test whether various chart formations are useful in projecting a security's future price movements.
 in losses is transmitted into prices, and both the numerator numerator

the upper part of a fraction.


numerator relationship
see additive genetic relationship.


numerator Epidemiology The upper part of a fraction
 and the denominator denominator

the bottom line of a fraction; the base population on which population rates such as birth and death rates are calculated.

denominator 
 of the ELR will be serially correlated. (18) In addition, the fact that policies are issued over the course of the year implies that calendar year accounting data w ill contain information on the current and previous year's pricing decisions. This implies additional serial correlation. That is, if losses follow an AR(1) process, [phi](L) = [[phi].sub.1]L, then the reported accounting data will follow an AR(2) process. This argument also holds if losses have a unit root (i.e., [[phi].sub.1] = 1) as in Cummins and Outreville (1987). The conclusion is that serial correlation in prices can arise from the combination of the stochastic process stochastic process

In probability theory, a family of random variables indexed to some other set and having the property that for each finite subset of the index set, the collection of random variables indexed to it has a joint probability distribution.
 of losses and insurance accounting practices. Serial correlation in premiums is not per se evidence of informational inefficiencies or market imperfections. If the financial pricing model holds, then the ELR should be stationary and serially correlated. (19)

Table 2 reports the results of augmented Dickey-Fuller tests In statistics and econometrics, an augmented Dickey-Fuller test (ADF) is a test for a unit root in a time series sample. It is an augmented version of the Dickey-Fuller test to accommodate some forms of serial correlation.  for unit roots. Panel A of Table 2 reports the results of tests assuming no time trend, and Panel B reports the results of tests assuming that there is a linear time trend. For all three price series, ELR1, ELR2, and ELR3, the unit root hypothesis is rejected against the alternative with and without the trend. The result that the prices series are I(0) implies that premiums are cointegrated with the discounted value of losses. All three interest rate series, TBR, MGB MGB Mini-Gastric Bypass
MGB Minor Groove Binder (molecular biology)
MGB Manual Gearbox
MGB Matthew Good Band
MGB May God Bless
MGB Medial Geniculate Body
MGB Medium Girder Bridge
MGB Motor Gun Boat
MGB Microsoft Global Briefing
, and LGB Noun 1. LGB - a smart bomb that seeks the laser light reflected off of the target and uses it to correct its descent; "laser-guided bombs cannot be used in cloudy weather"
laser-guided bomb
, have unit roots. Two of the surplus series, BCAP and BSA, also have unit roots. The third, RC, is stationary around a positive linear time trend. Tests on log transformations of the variables yield the same conclusions.

The economic model, the FQH, and the OP model all imply that surplus is a long-run determinant of prices; that is, prices should be cointegrated with surplus. A necessary condition for two variables to be cointegrated is that they both must be I(1). The three ELR series are all I(0), and it follows that they cannot be cointegrated with surplus. The economic model, the FQH, and the OP approach are not consistent with the results of the unit root tests. The results here are consistent with those of Choi and Thistle (1999) and Higgins Higgins may refer to:

People with the surname Higgins:
  • Higgins (surname)
Other:
  • Higgins Armory Museum, in Worcester, Massachusetts, USA
  • Higgins boat, a landing craft used in amphibious warfare
 and Thistle (2000), who find that surplus is not a determinant of underwriting profits in the long run.

[y.sub.t] = [[alpha].sub.0] + [[alpha].sub.1]t + [[phi].sub.1][y.sub.t-1] + [[phi].sub.2][y.sub.t-2] + [[beta].sub.1][DELTA][r.sub.t] + [[beta].sub.2][DELTA][r.sub.t-1] + [[gamma].sub.1][DELTA][S.sub.t] + [[gamma].sub.2][DELTA][S.sub.t-1] + [[epsilon].sub.t], (8)

We now turn to the analysis of the short-run dynamics. The theoretical models imply that the short-run dynamics are driven by the interest rate, surplus, and conditional variance. We begin with the regression regression, in psychology: see defense mechanism.
regression

In statistics, a process for determining a line or curve that best represents the general trend of a data set.
 

where y, is the ELR in period t. There are two reasons to include the change in the interest rate in the regression. First, the general form of the theoretical model of prices, Equation 7, implies that it should be included. Second, the dependent variable is the ratio of an estimate of discounted losses to premiums and is certainly measured with error; inclusion of the change in the interest rate may capture the measurement error. The argument for not including the change in the interest rate is that the interest rate is used in the construction of the dependent variable, and the regression will simply reflect this. This suggests the empirical results concerning the effects of interest rate changes need to be interpreted cautiously. The equation is initially estimated by OLS OLS Ordinary Least Squares
OLS Online Library System
OLS Ottawa Linux Symposium
OLS Operation Lifeline Sudan
OLS Operational Linescan System
OLS Online Service
OLS Organizational Leadership and Supervision
OLS On Line Support
OLS Online System
. We then eliminate variables whose coefficients are not significantly different from zero and reestimate the model. The insurance industry has changed substantially over the period 1935-1997, suggesting that the model may not be stable ov er the entire sample period. We carry Out Chow tests The Chow test is an econometric test of whether the coefficients in two linear regressions on different data are equal. The Chow test is most commonly used in time series analysis to test for the presence of a structural break.  for structural shifts in 1950, 1966, and 1981 (the 1/4, 1/2, and 3/4 points in the sample) and include dummy variables This article is not about "dummy variables" as that term is usually understood in mathematics. See free variables and bound variables.

In regression analysis, a dummy variable
 if the hypothesis of parameter (1) Any value passed to a program by the user or by another program in order to customize the program for a particular purpose. A parameter may be anything; for example, a file name, a coordinate, a range of values, a money amount or a code of some kind.  stability is rejected at the 10% level. We carry out the analysis for each of the dependent variables, ELR1, ELR2, and ELR3, with each of the measures of surplus. The interest rate and the measures BCAP and BSA are I(1) and are entered in difference form, while RC is I(0) and is entered in levels.

The results of the analysis are reported in Table 3 for the price variable ELR1; the results for the log transformations are similar. The first three columns report the estimates for the full model, and the second three columns report the results following the reduction of the model. The estimates reported in column 6 imply that ELR1 follows an AR(1) process and is not cyclical. The estimates reported in columns 4 and 5 imply that ELR1 follows an AR(2) process where the period of the cycle is between seven and eight years. (20) For all three models in columns 4 to 6, the change in the interest rate is negative. This implies that an increase in the change in the short-term Short-term

Any investments with a maturity of one year or less.


short-term

1. Of or relating to a gain or loss on the value of an asset that has been held less than a specified period of time.
 interest rate leads to higher prices. This is consistent with the OP approach but not with the other models. It is also consistent with the view that the coefficient coefficient /co·ef·fi·cient/ (ko?ah-fish´int)
1. an expression of the change or effect produced by variation in certain factors, or of the ratio between two different quantities.

2.
 reflects the definition of the dependent variable or that it is capturing measurement error. (21) The coefficients for the lagged values of the surplus measures [DELTA]BCAP, [DE LTA LTA Land Transport Authority
LTA Land Trust Alliance
LTA Lawn Tennis Association
LTA Lost Time Accident
LTA Lighter-Than-Air
LTA Lieutenant (Singapore military)
LTA Lipoteichoic Acid
LTA Lymphotoxin-Alpha
]BSA, and RC are all positive, implying that increases in surplus growth or surplus lower prices although with a delay. This is consistent with all the models except the financial pricing and OP models. The results for ERL See URL. 2 and ELR3 and their log transformations are similar, except that the lagged interest rate change is significant when RC is used as the capacity measure. The change in the interest rate is not significant when [DELTA]BCAP or [DELTA]BSA is used as the capacity measure.

The results in Table 3 are the most directly comparable to previous studies. The definition of the ELRs, together with the fact that they are stationary, implies that the cointegrating relationships are taken account of in the estimates in Table 3. These results are consistent with those of Haley Ha·ley   , Alex 1921-1992.

American writer best known for Roots (1976), a fictionalized chronicle tracing his family history back to its African origins.

Noun 1.
 (1993, 1995), Grace and Hotchkiss Hotchkiss may refer to:
  • Benjamin B. Hotchkiss - a 19th century American engineer
  • Hotchkiss et Cie - Hotchkiss Company, a French arms and car manufacturer set up by Benjamin Hotchkiss; full name: Société Anonyme des Anciens
 (1995), and Choi and Thistle (1999), who find that measures of underwriting profits are cointegrated with interest rates. The models of the short-run dynamics in Table 3 are "balanced" in the sense that all variables are I(0). A number of previous studies have not treated the econometric e·con·o·met·rics  
n. (used with a sing. verb)
Application of mathematical and statistical techniques to economics in the study of problems, the analysis of data, and the development and testing of theories and models.
 issues created by the possibility of unit roots appropriately. First, many studies ignore the possibility of cointegrating relationships among the variables. For models estimated in difference form, this implies that all effects are constrained to be short-run effects. Also, if the error correction terms are important, these models suffer from omitted variable bias. Examples include Niehaus and Terry (1993) and Fung et al. (1998). The second problem is the use of unbalanced regressions, in which the regressand and regressors are not all I(0). For example, Gron (1994b) and Winter (1994) estimate regressions in which the dependent variable is apparently I(0) but one or more regressors is I(1). This implies (i) that the population regression coefficients Regression coefficient

Term yielded by regression analysis that indicates the sensitivity of the dependent variable to a particular independent variable. See: Parameter.


regression coefficient 
 on the I(1) variables are zero and (ii) that the estimated regression coefficients on the remaining I(0) regressors have nonstandard non·stan·dard  
adj.
1. Varying from or not adhering to the standard: nonstandard lengths of board.

2.
 distributions, so that inference (logic) inference - The logical process by which new facts are derived from known facts by the application of inference rules.

See also symbolic inference, type inference.
 based on asymptotic normal approximations is invalid Null; void; without force or effect; lacking in authority.

For example, a will that has not been properly witnessed is invalid and unenforceable.


INVALID. In a physical sense, it is that which is wanting force; in a figurative sense, it signifies that which has no effect.
. (22)

The OLS estimates in Table 3 restrict the variance of the error term to be constant. All the theoretical models except the financial pricing model imply that in the short run the economic loss ratio should depend on the conditional variance through the disequilibrium component or risk premium R in Equation 7. The risk premium depends on the conditional variance, [h.sub.t] = var{[[epsilon].sub.t]/[I.sub.t]}, and this variance changes over time as the conditioning information evolves. Since past errors are contained in this information set, this implies that the errors are generated by an autoregressive Autoregressive

Using past data to predict future data.

Notes:
Essentially it's forecasting, similar to the weather... Sometimes even the weatherman can be caught in an unexpected downpour.
 conditionally heteroskedastic Heteroskedastic

A measure in statistics that refers to the variance of the errors over the sample.

Notes:
Most financial instruments, such as stocks, follow a heteroskedastic error pattern.
 (ARCH) process. The errors are generated by an ARCH process if

[[epsilon].sub.t] = [[zeta].sub.t]/[h.sub.t], (9)

where

[[zeta].sub.t]i.i.d(0, 1) and [h.sub.t] = h([I.sub.t-1], [theta Theta

A measure of the rate of decline in the value of an option due to the passage of time. Theta can also be referred to as the time decay on the value of an option. If everything is held constant, then the option will lose value as time moves closer to the maturity of the option.
]).

Since the theory implies that this conditional variance is a regressor, the appropriate model is an ARCH-in-mean (ARCH-M) model.

The statistical properties of the ARCH process depend on the form of the variance equation h([I.sub.t-1], [theta]). We employ the GARCH GARCH Generalized Autoregressive Conditional Heteroskedasticity  process, for which the variance function can be written as

[h.sub.t] = [[theta].sub.0] + [[theta].sub.1][[epsilon].sub.t-1] + [[theta].sub.2][h.sub.t-1]. (10)

We also employ the EGARCH process, for which the variance function can be written as

ln([h.sub.t]) = [[theta].sub.0] + [[theta].sub.1][\[[zeta].sub.t-1]\ - [square root of ((2/[pi]))] + [[theta].sub.2][[zeta].sub.t-1] + [[theta].sub.3]ln([h.sub.t-1]), (11)

where [[zeta].sub.t] = [[epsilon].sub.t]/h, is the normalized error, assumed to be N(0, 1), and [square root of (2/[pi])] = E{\[[zeta].sub.t]\}. The expression in Equation 11 is linear in the shocks, with slope [[theta].sub.1] + [[theta].sub.2] if the shock is positive and slope [[theta].sub.2] - [[theta].sub.1] if the shock is negative. For both these ARCH processes, the unconditional HEIR, UNCONDITIONAL. A term used in the civil law, adopted by the Civil Code of Louisiana. Unconditional heirs are those who inherit without any reservation, or without making an inventory, whether their acceptance be express or tacit. Civ. Code of Lo. art. 878.

UNCONDITIONAL.
 distributions of the [[epsilon].sub.t] are nonnormal and leptokurtic (thick-tailed) with mean zero and constant variance. The [[epsilon].sub.t], are also serially uncorrelated; however, there is dependence through the higher moments. (23)

We reestimate the regression in Equation 8 with the addition of the conditional variance or the conditional standard error as a regressor, assuming the GARCH process in Equation 10 or the EGARCH process in Equation 11 for the conditional variance. We tested for time trends and for shifts in the variance equation in 1950, 1966, and 1981.

Table 4 reports the results for ELR1, ELR2, and ELR3 and their log transformations using [DELTA]BCAP as the measure of surplus and assuming the EGARCH specification in which the conditional standard deviation In statistics, the average amount a number varies from the average number in a series of numbers.

(statistics) standard deviation - (SD) A measure of the range of values in a set of numbers.
 enters the mean equation. First, there is some evidence of structural shifts over the sample period with the shifts in the mean equation occurring in 1966 or 1981 and the shifts in the variance equation occurring in 1950 or 1966. For the dependent variables ELRl and ELR2, there is no cycle. For the remaining variables, the estimated period is from 9 to 12 years, which is somewhat longer than most estimates. The interest rate coefficient This article or section may be confusing or unclear for some readers.
Please [improve the article] or discuss this issue on the talk page.
 is negative and not significant in the equations for LNELR2 and LNELR3 but is negative and significant in the equations for the other dependent variables. The effect of [DELTA]BCAP on the ELR is positive; however, it is the lagged rather than the current value, which is significant. The conditional standard deviation is significant and negative in the all the equations. T hese results imply that an increase in surplus growth leads to lower prices and that an increase in the conditional variance leads to higher prices. Taken together, the results for surplus growth and the conditional variance are consistent with the actuarial model and CCH and are not consistent with the financial pricing model or the OP model.

The results using [DELTA]BSA and RC as the measure of surplus are reported in Tables 5 and 6. The results are qualitatively similar. In both cases, there is evidence of shifts in the mean equation in 1981 and of shifts in the variance equation in 1950 or 1966. In Table 5, estimated length of the cycle is over 18 years for ELR2 and almost 14 years for ELR3, while in Table 6 the estimated length of the cycle is 13.5 years for LNELR1. In the remaining cases, the estimated cycle length ranges from seven to nine years. In Table 5, the change in the T-bill rate has a significant negative effect on ELR1 and LNELRl; the changes in the medium- and long-term interest rates are negative but not significant. In Table 6, the effect of the change in the interest rate is never significant. In Table 5, the capacity measure [DELTA]BSA has a significant positive effect, except in the equation for ELR2. In Table 6, the capacity measure RC has a significant positive effect in the equations for ELR2 and LNELR1; the effect is posi tive but not significant in the other equations. The conditional variance has a significant negative effect for all but one of the equations in Table 5 and for all but two of the equations in Table 6. In the remaining equations, the effect is negative but not significant. Again, the empirical results imply that an increase in surplus or surplus growth leads to lower prices and that an increase in the conditional variance leads to higher prices.

The results reported in Tables 4 to 6 assume the EGARCH specification of the variance equation and that the conditional standard deviation enters the mean equation. Using the GARCH specification of the variance equation and assuming the conditional standard deviation enters the mean equation yields qualitatively similar results. Using either specification of the variance equation and assuming that the conditional variance enters the mean equation yields weaker support for a significant effect for the conditional variance. Our view is that the specification using the conditional standard deviation is more appropriate.

Taken overall, we find no evidence that an increase in the conditional variance (or standard deviation) leads to lower prices in the short run, as implied by the OP model. We find no evidence that an increase in surplus leads to higher prices in the short run, another implication of the OP model. The financial pricing model implies that neither the conditional variance nor the surplus should affect prices in the short run. We find substantial empirical evidence that an increase in surplus or surplus growth leads to lower prices in the short run and that an increase in the conditional standard deviation increases prices in the short run. The actuarial model and the capacity constraint hypothesis are the only theoretical models that are consistent with the empirical results in both the long and the short run.

4. Conclusion

Property/liability insurance markets are subject to an underwriting cycle, alternating between soft markets, with low prices and profits, and hard markets, with sharply higher prices and profits. The insurance literature has tended to rely on institutional and regulatory features specific to insurance markets and capital market imperfections to explain recurrent hard markets. Consequently, the empirical literature has focused on testing between the financial pricing and capacity constraint models.

In this paper, we carry out an empirical comparison of six alternative models of insurance pricing as theories of the underwriting cycle using data on stock property/liability insurance companies for 1935-1997. We show that all the models imply the same general form for the pricing equation. All the models imply that the price of insurance equals the costs of issuing the policy plus the expected present value of losses plus an additional term that, in general, depends in the interest rate, surplus, and the conditional variance of losses. In the long run, the additional term can be interpreted as either a risk premium or a quality premium, depending on the theoretical model. In the short run, the additional term can be interpreted as a risk premium, a quality premium, or the disequilibrium component of prices, again depending on the theoretical model. The role of the conditional variance in this additional term has not been emphasized in the literature, and its presence has not been tested for. It is important to distinguish theoretically between the long- and the short-run implications of the models. Four of the six models have the same implications for the short-run behavior of prices. This implies that it is important to distinguish econometrically between long-run cointegrating relationships and short-run dynamics. Both need to be examined in order to test between the models.

We employ the ELR as an inverse (mathematics) inverse - Given a function, f : D -> C, a function g : C -> D is called a left inverse for f if for all d in D, g (f d) = d and a right inverse if, for all c in C, f (g c) = c and an inverse if both conditions hold.  measure of insurance prices. The ELR is the ratio of an estimate of the present value of losses to premiums net of expenses; three discount rates are used to compute To perform mathematical operations or general computer processing. For an explanation of "The 3 C's," or how the computer processes data, see computer.  ELRs. Three measures of surplus, the ratio of surplus to premiums written, the ratio of surplus to assets, and the ratio of surplus to a lagged moving average are used. We find that the ELRs do not have a unit root. This implies (i) that premiums are cointegrated with discounted losses and (ii) that the ELRs cannot be cointegrated with surplus. Since both the economic model and the financial quality hypothesis imply that there should be a long-run relationship between premiums and surplus, these models are not consistent with the data. We find substantial empirical evidence that an increase in surplus or surplus growth leads to lower prices in the short run and that an increase in the conditional standard deviation increases prices in the short run. The financial pricing model implies that premiums should not depen d on surplus or the conditional standard deviation. The contingent claims analysis model implies that price should increase with surplus and decrease with the conditional standard deviation. These models are not consistent with the data. The actuarial model and the capacity constraint hypothesis are the only theoretical models that are consistent with the empirical results in both the long and the short run.

These results have implications for public policy toward the insurance industry. First, and perhaps most important, in the long run, premiums are determined by policy expenses and the expected present value of claims. One implication of this result is that hard markets, such as the episodes in the 1970s and 1980s, are short-run phenomena. Another implication of this result is that legal and regulatory decisions that increase insurers' claim costs will ultimately lead to higher prices for insurance. Second, surplus is an important determinant of prices in the short run, with higher levels of surplus or surplus growth leading to lower prices. Much of insurance regulation is intended so that insurers are adequately capitalized Capitalized

Recorded in asset accounts and then depreciated or amortized, as is appropriate for expenditures for items with useful lives longer than one year.
. To the extent that it is successful, such regulation may lead to lower prices, although only in the short run. Finally, we find that the conditional variance is also an important determinant of prices, with higher conditional variances leading to higher prices. An important implication of this result is that policy decisions that increase insurance market volatility lead to higher prices for insurance, at least in the short run.

(*.) Department of Finance, University of Nevada, Las Vegas “UNLV” redirects here. For other uses, see UNLV (disambiguation).
The University of Nevada, Las Vegas (UNLV) is a public, coeducational university located in Las Vegas, Nevada, USA, known for its programs in History, Engineering, Environmental Studies, Hotel
, 4505 Maryland Maryland (mâr`ələnd), one of the Middle Atlantic states of the United States. It is bounded by Delaware and the Atlantic Ocean (E), the District of Columbia (S), Virginia and West Virginia (S, W), and Pennsylvania (N).  Parkway, Box 456008, Las Vegas Las Vegas (läs vā`gəs), city (1990 pop. 258,295), seat of Clark co., S Nev.; inc. 1911. It is the largest city in Nevada and the center of one of the fastest-growing urban areas in the United States. , NV 89154-6008, USA.

(+.) Department of Finance, University of Nevada, Las Vegas, 4505 Maryland Parkway, Box 456008, Las Vegas, NV 89154-6008, USA: E-mail pthistle@ccmail ccmail - It's written cc:mail. .nevada Nevada (nəvăd`ə, –vä–), far western state of the United States. It is bordered by Utah (E), Arizona (SE), California (SW, W), and Oregon and Idaho (N). .edu See .edu.

(networking) edu - ("education") The top-level domain for educational establishments in the USA (and some other countries). E.g. "mit.edu". The UK equivalent is "ac.uk".
; corresponding author.

The authors would like to thank Mast Higgins, James James, person in the Bible
James, in the Gospel of St. Luke, kinsman of St. Jude. The original does not specify the relationship.
James, rivers, United States
James.
 Ligon, Don Meyer Mey·er   , Annie Florance Nathan 1867-1951.

American writer and a founder of Barnard College at Columbia University (1889). Her plays include The Dominant Sex (1911) and Black Souls (1932).
, Alan A`lan´   

n. 1. A wolfhound.
 Schlottmann, Mary Mary, the mother of Jesus
Mary, in the Bible, mother of Jesus. Christian tradition reckons her the principal saint, naming her variously the Blessed Virgin Mary, Our Lady, and Mother of God (Gr., theotokos). Her name is the Hebrew Miriam.
 Riddel, and Brad Wimmer for constructive comments on earlier versions of this paper as well as two anonymous referees whose comments have substantially improved the paper. The authors acknowledge financial assistance from First Interstate in·ter·state  
adj.
Involving, existing between, or connecting two or more states.

n.
One of a system of highways extending between the major cities of the 48 contiguous United States.

Noun 1.
 Bank Institute for Business Leadership. Thistle's research was partially supported by the Faculty Research and Creative Activities Support Fund, Western Michigan University Western Michigan University, at Kalamazoo, Mich.; coeducational; founded in 1903 as Western State Normal School, became accredited in 1927 as a college, gained university status in 1957. , while he was on the faculty there. Our friend and colleague Don Hardigree passed away in December December: see month.  1997.

Received October October: see month.  1998; accepted July July: see month.  2001.

(1.) See, for example, Winter (1991a, pp. 118-20) for a description of the typical features of a hard market and Harrington (1988) for a discussion of the financial performance of the property/liability insurance industry focusing on the 1975-1977 and 1984-1987 periods. Cycles are not unique to the U.S. insurance industry. See Cummins and Outerville (1987), Lamm-Tennant and Weiss (1997), and Chen, Wong n. 1. A field. , and Lee (1999) on international comparisons.

(2.) Winter (1991b) argues that solvency regulation based on premium/surplus ratios exacerbates the problem.

(3.) See also Niehaus and Terry (1993), Haley (1993, 1995), Grace and Hotchkiss (1995), Fung et al. (1998). Choi and Thistle (1999), and Higgins and Thistle (2000) for additional empirical evidence.

(4.) Sommers's (1996) cross-sectional cross section also cross-sec·tion
n.
1.
a. A section formed by a plane cutting through an object, usually at right angles to an axis.

b. A piece so cut or a graphic representation of such a piece.

2.
 results are consistent with the OP model.

(5.) The distribution of losses per policy does not depend on the number of policies sold; that is, the firm sells enough policies to eliminate idiosyncratic risk Idiosyncratic Risk

Risk that affects a very small number of assets, and can be almost eliminated with diversification. Similar to unsystematic risk.

Notes:
This is news that is specific to a small number of stocks. One example is a sudden strike by employees.
. The randomness in the average claim per policy arises from dependence in losses across policies. As long as losses are less than perfectly positively correlated across policies, the per-policy variance will decrease with the number of policies to some limit, That is, losses must obey Obey can refer to:
*Obedience, the act of following instructions or recognizing someone's authority.
*André Obey, the 20th century French playwright.
*David Obey, US Congressman from Wisconsin.
 a law of large numbers Law of large numbers

The mean of a random sample approaches the mean (expected value) of the population as sample size increases.
 (see, e.g., Marshall Marshall.

1 City (1990 pop. 12,711), seat of Saline co., N central Mo.; inc. 1839. In a large farm area, it is a processing center for grain, eggs, meat, and dairy products. Marshall is the seat of Missouri Valley College.
 1974).

(6.) These adjustment costs arise from asymmetries in the tax code (due to the taxation of dividends) and from asymmetric information Asymmetric Information

Information available to some people but not others.

Notes:
In other words, the asymmetric information is held by only one side, meaning someone is keeping a secret.
 between management and the market regarding the future prospects of the firm. See, for example, Winter (1988, pp. 472-5) for a more complete discussion.

(7.) In Harrington and Danzon and in Cagle and Harrington, [KAPPA] is the value of the firm's intangible capital or franchise value that is lost as a result of bankruptcy. Intangible capital includes, for example, the value of reputation and the quasi-rent Quasi-rent is an analytical term in economics, for the income earned, in excess of post-investment opportunity cost, by a sunk cost investment. Alfred Marshall (1842-1924) was the first to observe quasi-rents.  on renewal business.

(8.) Harrington and Danzon (1994) focus on possible reasons for underpricing Underpricing

Issuing securities at less than their market value.


underpricing

The pricing of a new security issue at less than the prevailing price of the same security in the secondary market. Underpricing helps ensure a successful sale.
 in the soft market phase of the cycle. They allow default risk to be endogenous but do not assume that demand depends on default risk.

(9.) Cagle and Harrington (1995) do not derive the effect of a second-degree stochastically sto·chas·tic  
adj.
1. Of, relating to, or characterized by conjecture; conjectural.

2. Statistics
a. Involving or containing a random variable or variables: stochastic calculus.
 dominating increase in the riskiness of the loss distribution. The sign of the effect depends on the third derivative derivative: see calculus.
derivative

In mathematics, a fundamental concept of differential calculus representing the instantaneous rate of change of a function.
 of the objective function, which is indeterminate That which is uncertain or not particularly designated.


INDETERMINATE. That which is uncertain or not particularly designated; as, if I sell you one hundred bushels of wheat, without stating what wheat. 1 Bouv. Inst. n. 950.
. Cummins and Danzon (1997) allow for two classes of policyholders and analyze the problem in an option theoretic framework. Their model implies that output depends on equity and the riskiness of the loss distributions but that the comparative statics effects are ambiguous.

(10.) The Cummins-Danzon. version of the FQH was originally developed as a model of cross-sectional differences in prices across firms. The usual interpretation of cross-sectional analysis Cross-sectional analysis

Assessment of relationships among a cross-section of firms, countries, or some other variable at one particular time.
 is that they capture long-run behavior. As we point out here, the FQH also has implications for both the short- and the long-run time-series behavior of prices.

(11.) See, for example, Beard beard, hair on the lower portion of the face. The term mustache refers to hair worn above the upper lip. Attitudes toward facial hair have varied in different cultures. , Pentikainen, and Pesonen (1984, p. 129), who refer to this as the "basic equation." The number of policies to be sold is exogenous Exogenous

Describes facts outside the control of the firm. Converse of endogenous.
; that is, the firm's demand is perfectly inelastic inelastic

Of or relating to the demand for a good or service when quantity purchased varies little in response to price changes in the good or service.
. Initial surplus is assumed to be exogenous, and surplus is accumulated ac·cu·mu·late  
v. ac·cu·mu·lat·ed, ac·cu·mu·lat·ing, ac·cu·mu·lates

v.tr.
To gather or pile up; amass. See Synonyms at gather.

v.intr.
To mount up; increase.
 only through retained earnings Retained Earnings

The percentage of net earnings not paid out in dividends, but retained by the company to be reinvested in its core business or to pay debt. It is recorded under shareholders equity on the balance sheet.
. The target probability of insolvency is also exogenous. The idea that the firms might choose to issue new equity or might pay dividends is simply absent from the analysis.

(12.) See, for example, Cummins (1991, p. 291, eqn. 54). Policyholders receive the indemnity Recompense for loss, damage, or injuries; restitution or reimbursement.

An indemnity contract arises when one individual takes on the obligation to pay for any loss or damage that has been or might be incurred by another individual.
 owed by the insurer if the insurer is solvent solvent, constituent of a solution that acts as a dissolving agent. In solutions of solids or gases in a liquid, the liquid is the solvent. In all other solutions (i.e. ; otherwise, they receive the insurer's assets, that is, min(A, xq), where A is assets. Observe that min(A, xq) = xq - max(xq - A, 0) and that the second term is the terminal payoff to a put option. The financial pricing model can be viewed as the special case of the OP approach in which there is no default risk, that is, B = 0. Also observe that there is an implicit assumption in the OP approach that capital markets are perfect.

(13.) The effect of a change in the interest rate may be determinate DETERMINATE. That which is ascertained; what is particularly designated; as, if I sell you my horse Napoleon, the article sold is here determined. This is very different from a contract by which I would have sold you a horse, without a particular designation of any horse. 1 Bouv. Inst. n. 947, 950.  for specific option pricing models option pricing model

A mathematical formula for determining the price at which an option should trade. The model expresses the value of an option as a function of the value of the underlying asset, length of time until maturity, exercise price, yields on
. For example, if the assumptions of the Black-Scholes model apply, an increase in the interest rate decreases the price of insurance.

(14.) Firms will exhibit risk-averse behavior if decisions are made by managers whose compensation and wealth depend on the performance of the firm and who may not be able so optimally diversify diversify

To acquire a variety of assets that do not tend to change in value at the same time. To diversify a securities portfolio is to purchase different types of securities in different companies in unrelated industries.
; compare Greenwald Greenwald may refer to:
  • Greenwald, Minnesota, USA
Greenwald as a surname may refer to:
  • Greenwald family, the rabbinic family of Puppa (Hasidic dynasty), originated from Hungary
  • Alex Greenwald
  • Andy Greenwald
 and Stiglitz (1990). In particular, if managers have firm-specific human capital, reputations, or shares in the firm, then the manager's wealth will be a concave function In mathematics, a real-valued function f defined on an interval (or on any convex set C of some vector space) is called concave, if for any two points x and y in its domain C and any t in [0,1], we have
 of the firm's performance.

(15.) In principle, we need data on accident year losses, which measures losses arising out of all accidents in a given year. These data are not available for the full sample period. In practice, we use data on calendar year loss, which measures all transactions during a given year. Calendar year losses are equal to accident year losses plus an adjustment for outstanding claims, both reported and unreported. See Heubner, Black, and Webb (1996, p. 625).

(16.) loss ratio is the ratio of incurred losses to premiums earned. Incurred losses is an estimate of claims payments associated with policies in force during the year, plus any adjustments to outstanding claims (both reported and incurred but not reported Incurred but not reported (IBNR) is a term in common use in general insurance.

When a policy of general insurance is written it will typically cover a 12 month period from inception of the policy.
) on expired ex·pire  
v. ex·pired, ex·pir·ing, ex·pires

v.intr.
1. To come to an end; terminate: My membership in the club has expired.

2.
 policies; estimates of future claims payments are not discounted. The expense ratio is the ratio of expenses incurred to premiums written. Premiums written are calculated on a cash basis; premiums earned are calculated on an accrual basis A method of accounting that reflects expenses incurred and income earned for Income Tax purposes for any one year.

Taxpayers who use the accrual method must include in their taxable income any money that they have the right to receive as payment for services, once it
. Most policies are for one year, so premiums earned will reflect policies sold in the current and previous years.

(17.) Liability insurance has grown in importance relative to property insurance over the sample period, so that the average lag between payment of the premium and claim settlement has increased. Since the [[beta].sub.s] are calculated from data relatively late in the sample period, this suggests that the present value of losses may be underestimated early in the sample period and that the degree of underestimation may change systematically over time. We include a time trend in our empirical analysis to adjust for this potential measurement error.

(18.) This is essentially the argument given in Cummins and Outreville (1987), who assume that shocks to losses have a permanent component; that is, losses have a unit root. This is not necessary for prices to be serially correlated.

(19.) Inclusion of lagged terms in the regression may also capture any unexplained unexplained
Adjective

strange or unclear because the reason for it is not known

Adj. 1. unexplained - not explained; "accomplished by some unexplained process"
 cyclical influences.

(20.) An AR(2) process is cyclical if [[phi].sup.2.sub.1] + 4[[phi].sub.2] < 0, in which case the period of the cycle is 2[pi]/arccos(\[[phi].sub.1]\/2[square root of (-[[phi].sub.2])]).

(21.) The measurement error arises from insurance accounting conventions that determine how the data are reported. The numerator of the ELR is D X LR, where D is the discount factor and LR is the loss ratio. The loss ratio is the ratio of losses incurred to premiums earned, where premiums earned are calculated on an accrual basis. An increase in interest rates decreases the present value of losses. Even if prices for new policies adjust, premiums earned, which are a combination of current and previous years' prices, will not fully reflect this, and the numerator of the ELR will decrease. Further, this effect is stronger the more rapidly interest rates change.

(22.) See Bancrjcc et al. (1993, pp. 164-8) for a discussion of this point.

(23.) The ARCH, generalized gen·er·al·ized
adj.
1. Involving an entire organ, as when an epileptic seizure involves all parts of the brain.

2. Not specifically adapted to a particular environment or function; not specialized.

3.
 ARCH (GARCH) models, and their extensions are developed in Engle En´gle

n. 1. A favorite; a paramour; an ingle.
v. t. 1. To cajole or coax, as favorite.
I 'll presently go and engle some broker.
- B. Jonson.
 (1982); Bollerslev (1986); Engle, Lilien, and Robins (1987); and Nelson (1991), among others. See Engle and Bollerslev (1986); Bollerslev, Chou Chou (jō), dynasty of China, which ruled from c.1027 B.C. to 256 B.C. The pastoral Chou people migrated from the Wei valley NW of the Huang He c.1027 B.C. and overthrew the Shang dynasty. The Chou built their capital near modern Xi'an in 1027 B.C. , and Kroner (1992); and Bera and Higgins (1993) for surveys.

References

Banerjee Banerjee (anglicized from Bandyopadhyay) is a prevalent brahmin surname in the Bengal region of India. Banerjees are kulin brahmins, the highest class in Bengal (along with Mukherjees, Chatterjees, Gangulis, and Ghoshals). , Anindya, Juan Juan (IPA: [xwan]) is a Spanish form of the given name John (q.v.). It was the 55th most popular name in the United States as of 2003.  Dolado, John W. Galbraith Gal·braith   , John Kenneth Born 1908.

Canadian-born American economist, writer, and diplomat who served as U.S. ambassador to India (1961-1963). His works include The Great Crash (1955).

Noun 1.
, and David F. Hendry Hendry may refer to:

People with the surname Hendry:
  • Billy Hendry, Scottish former football player
  • Charles Hendry, English politician
  • Colin Hendry, Scottish former professional football player
  • J. F. Hendry, Scottish poet
  • Jim Hendry, U.S.
. 1993. Cointegration Cointegration is an econometric property of time series variables. If two or more series are themselves non-stationary, but a linear combination of them is stationary, then the series are said to be cointegrated. , error Correction and the econometric analysis of non-stationary data. New York New York, state, United States
New York, Middle Atlantic state of the United States. It is bordered by Vermont, Massachusetts, Connecticut, and the Atlantic Ocean (E), New Jersey and Pennsylvania (S), Lakes Erie and Ontario and the Canadian province of
: Oxford University Press.

Beard, R. E., T. Pentikainen, and E. Pesonen. 1984. Risk theory: The stochastic By guesswork; by chance; using or containing random values.

stochastic - probabilistic
 basis of insurance. 3rd edition. London London, city, Canada
London, city (1991 pop. 303,165), SE Ont., Canada, on the Thames River. The site was chosen in 1792 by Governor Simcoe to be the capital of Upper Canada, but York was made capital instead. London was settled in 1826.
: Chapman and Hall Chapman and Hall was a British publishing house, founded in the first half of the 19th century by Edward Chapman and William Hall. Upon Hall's death in 1847, Chapman's cousin Frederic Chapman became partner in the company, of which he became sole manager upon the retirement of .

Bera, Anil K., and Matthew Matthew

one of the twelve disciples. [N.T.: Matthew]

See : Evangelism
 L. Higgins. 1993. ARCH models: Properties, estimation estimation

In mathematics, use of a function or formula to derive a solution or make a prediction. Unlike approximation, it has precise connotations. In statistics, for example, it connotes the careful selection and testing of a function called an estimator.
 and testing. Journal of Economic Surveys 7:305-66.

Best's aggregates and averages, property casualty insurance edition. Various editions. Oldwick, NJ: A.M. Best Company.

Bollerslev, Timothy. 1986. Generalized autoregressive conditional heteroscedasticity heteroscedasticity

an irregular scattering of values in a series of distributions; accompanied by a comparable scatter of variances.
. Journal of Econometrics econometrics, technique of economic analysis that expresses economic theory in terms of mathematical relationships and then tests it empirically through statistical research.  31:307-27.

Bollerslev, Timothy, R. Y. Chou, and Kenneth F Kroner. 1992. ARCH modelling in finance: A review of the theory and empirical evidence. Journal of Econometrics 52:5-59.

Cagle, Julie JULIE Joint Utility Locating Information for Excavators
JULIE Jena University Language and Information Engineering (Germany) 
 A. B., and Scott Harrington Scott Harrington (born December 24, 1963 in Louisville, Kentucky) is a former driver in the Indy Racing League. Starting out on two wheels, Harrington won a number of championships and achieved much success in the world of AMA Motocross and Supercross. . 1995. Insurance supply with capacity constraints CONSTRAINTS - A language for solving constraints using value inference.

["CONSTRAINTS: A Language for Expressing Almost-Hierarchical Descriptions", G.J. Sussman et al, Artif Intell 14(1):1-39 (Aug 1980)].
 and endogenous insolvence risk. Journal of Risk and Uncertainty 11:219-32.

Chen, Renbao, Kie Ann ANN, Scotch law. Half a year's stipend over and above what is owing for the incumbency due to a minister's relict, or child, or next of kin, after his decease. Wishaw. Also, an abbreviation of annus, year; also of annates. In the old law French writers, ann or rather an, signifies a year.  Wong, and Hong n. 1. A mercantile establishment or factory for foreign trade in China, as formerly at Canton; a succession of offices connected by a common passage and used for business or storage.  Chew Lee. 1999. Underwriting cycles in Asia. Journal of Risk and insurance 66:29-47.

Choi, Seungmook, and Paul D. Thistle. 1999. A structural approach to underwriting cycles in the property/liability insurance industry. Working Paper. Department of Finance, University of Nevada-Las Vegas.

Cummins, J. David. 1991. Statistical and financial models of insurance pricing and the insurance firm. Journal of Risk and insurance 58:261-302.

Cummins, J. David, and Patricia M. Danzon. 1997. Price, financial quality and capital flows in insurance markets. Journal of Financial Intermediation 6:3-38.

Cummins, J. David, and J. Francois Outreville. 1987. An international analysis of underwriting cycles in property-liability insurance. Journal of Risk and insurance 54:246-62.

Doherty, Neil A., and James R. Garven. 1995. Insurance cycles: Interest rates and the capacity constraint hypothesis. Journal of Business 68:383-404.

Doherty, Neil A., and Han Han, Chinese dynasty
Han (hän), dynasty of China that ruled from 202 B.C. to A.D. 220. Liu Pang, the first Han emperor, had been a farmer, minor village official, and guerrilla fighter under the Ch'in dynasty.
 Bin Kang. 1988. Interest rates and the insurance price cycle. Journal of Banking and Finance 12:199-215.

Doherty, Neil A., and Lisa L. Posey. 1993. Availability crises in insurance markets: Optimal contracts with asymmetric information and capacity constraints. Working Paper, University of Pennsylvania (body, education) University of Pennsylvania - The home of ENIAC and Machiavelli.

http://upenn.edu/.

Address: Philadelphia, PA, USA.
.

Engle, Robert Robert, Henry Martyn 1837-1923.

American army engineer and parliamentary authority. He designed the defenses for Washington, D.C., during the Civil War and later wrote Robert's Rules of Order (1876).

Noun 1.
. 1982. Autoregressive conditional heteroscedasticity with estimates of the variance of UK inflation. Econometrica Econometrica is an academic journal of economics, publishing articles not only in econometrics but in many areas of economics. It is published by the Econometric Society via Blackwell Publishing.  50:987-1008.

Engle, Robert, and Timothy Bollerslev. 1986. Modelling the persistence (1) In a CRT, the time a phosphor dot remains illuminated after being energized. Long-persistence phosphors reduce flicker, but generate ghost-like images that linger on screen for a fraction of a second.  of conditional variances. Econometric Reviews 5:1-87.

Engle, Robert, David Lilien, and Russell Russell, English noble family. It first appeared prominently in the reign of Henry VIII when

John Russell, 1st earl of Bedford, 1486?–1555, rose to military and diplomatic importance.
 P. Robins. 1987. Estimating time varying risk premia Premia is a comune (municipality) in the Province of Verbano-Cusio-Ossola in the Italian region Piedmont, located about 140 km northeast of Turin and about 40 km northwest of Verbania, on the border with Switzerland.  in the term structure: The ARCH-M model. Economnetrica 55:391-407.

Fung, Hung-Gay, Gene C. Lai, Gary Gary, city (1990 pop. 116,646), Lake co., NW Ind., a port of entry on Lake Michigan; inc. 1909. Gary was founded by the U.S. Steel Corporation, which purchased the land in 1905 and landscaped it for a city.  A. Patterson Patterson, family of American journalists.

Robert Wilson Patterson, 1850–1910, b. Chicago, grad. Williams, 1871, became (1871) a reporter on the Chicago Times and after 1873 was attached to the Chicago Tribune.
, and Robert C. Witt Witt   , Katerina Born 1965.

German figure skater who won gold medals at the 1984 and 1988 Olympic games. She won world championships in 1985, 1987, and 1988.
. 1998. Underwriting cycles in property and liability insurance: An empirical analysis of industry and by-line data. Journal of Risk and insurance 65:539-61.

Greenwald, Bruce Bruce, Scottish royal family descended from an 11th-century Norman duke, Robert de Brus. He aided William I in his conquest of England (1066) and was given lands in England.  C., and Joseph E. Stiglitz Joseph Eugene "Joe" Stiglitz (born February 9, 1943) is an American economist and a member of the Columbia University faculty. He is a recipient of the John Bates Clark Medal (1979) and the Nobel Memorial Prize in Economics (2001). . 1990. Asymmetric information and the new theory of the firm: Financial constraints and risk behavior. American American, river, 30 mi (48 km) long, rising in N central Calif. in the Sierra Nevada and flowing SW into the Sacramento River at Sacramento. The discovery of gold at Sutter's Mill (see Sutter, John Augustus) along the river in 1848 led to the California gold rush of  Economic Review 80:160-5.

Grace, Martin F., and Julie Hotchkiss. 1995. External impacts on the property-liability insurance cycle. Journal of Risk and insurance 62:110-27.

Gron, Anne Anne, British princess
Anne (Anne Elizabeth Alice Louise), 1950–, British princess, only daughter of Queen Elizabeth II and Prince Philip, duke of Edinburgh. She was educated at Benenden School.
. 1994a. Capacity constraints and cycles in property-casualty insurance markets. Rand Rand  

See Witwatersrand.



rand 1  
n.
See Table at currency.



[Afrikaans, after(Witwaters)rand.
 Journal of Economics 25:110-27.

Gron, Anne. 1994b. Evidence of capacity constraints in insurance markets. Journal of Law and Economics 37:349-77.

Gron, Anne. 1995. Collusion An agreement between two or more people to defraud a person of his or her rights or to obtain something that is prohibited by law.

A secret arrangement wherein two or more people whose legal interests seemingly conflict conspire to commit Fraud
, costs or capacity? Evaluating theories of insurance cycles. Working Paper, Northwestern University Northwestern University, mainly at Evanston, Ill.; coeducational; chartered 1851, opened 1855 by Methodists. In 1873 it absorbed Evanston College for Ladies. .

Haley, Joseph D. 1993. A cointegration analysis of the relationship between underwriting margins and interest rates, 1930-1989. Journal of Risk and insurance 60:480-93.

Haley, Joseph D. 1995. A by-line cointegration analysis of underwriting margins and interest rates in the property liability industry. Journal of Risk and Insurance 62:755-63.

Harrington. Scott E. 1988. Prices and profits in the liability insurance markets. In Liability: Perspectives and policy, edited by Robert E. Litan and C. Winston Winston is a name deriving from Old English wynnstān, meaning "pleasant stone". Places
Winston is the name of several places in England:
  • Winston, County Durham
  • Winston, Suffolk
- and in the United States of America:
. Washington Washington, town, England
Washington, town (1991 pop. 48,856), Sunderland metropolitan district, NE England. Washington was designated one of the new towns in 1964 to alleviate overpopulation in the Tyneside-Wearside area.
, DC: Brookings Institute, pp. 42-100.

Harrington, Scott E., and Patricia M. Danzon. 1994. Price cutting in liability insurance markets. Journal of Business 67:511-38.

Huebner, S. S., Kenneth Black, Jr., and Bernard Ber·nard , Claude 1813-1878.

French physiologist noted for his study of the digestive and nervous systems.
 L. Webb. 1996. Property and liability insurance. 4th edition. Upper Saddle River Saddle River may refer to:
  • Saddle River, New Jersey, a borough in Bergen County, New Jersey
  • Saddle River (New Jersey), a tributary of the Passaic River in New Jersey
, NJ: Prentice Hall Prentice Hall is a leading educational publisher. It is an imprint of Pearson Education, Inc., based in Upper Saddle River, New Jersey, USA. Prentice Hall publishes print and digital content for the 6-12 and higher education market. History
In 1913, law professor Dr.
.

Higgins, Matthew L., and Paul D. Thistle. 2000. Capacity constraints and the dynamics of underwriting profits. Economic Inquiry 38:442-57.

Lamm-Tennant, Joan, and Mary Weiss. 1997. International insurance cycles: Rational expectations/institutional intervention A procedure used in a lawsuit by which the court allows a third person who was not originally a party to the suit to become a party, by joining with either the plaintiff or the defendant. . Journal of Risk and Insurance 64:415-39.

Leland, Hayne E. 1972. Theory of the firm facing uncertain demand. American Economic Review 62:278-91.

Marshall, John Marshall, John, 1755–1835, American jurist, 4th Chief Justice of the United States (1801–35), b. Virginia. Early Life


The eldest of 15 children, John Marshall was born in a log cabin on the Virginia frontier (today in Fauquier co., Va.
. 1974. Insurance theory: Reserves versus mutuality. Economic inquiry 12:476-92.

Myers, Stewart, and R. Cohn. 1987. Insurance rate regulation and the capital asset pricing model. In Fair Rate of Return in Property-Liability insurance, edited by J. David Cummins and Scott E. Harrington. Norwell, MA: Kluwer Academic Publishers, pp. 55-78.

Nelson, Daniel B. 1991. Conditional heteroskedasticity in asset returns: A new approach. Econometrica 59:347-70.

Niehaus, Greg, and A. Terry. 1993. Evidence on the time series properties of insurance premiums and causes of the underwriting cycle: New support for the capital markets imperfections hypothesis. Journal of Risk and Insurance 60:466-79.

Sandmo, A. 1971. On the theory of the competitive firm under price uncertainty. American Economic Review 61:65-73.

Sommers, David W. 1996. The impact of firm risk on property-liability insurance prices. Journal of Risk and insurance 63:501-14.

Winter, Ralph A. 1988. The liability crisis and the dynamics of competitive insurance markets. Yale Journal on Regulation 5:455-99.

Winter, Ralph A. 1991a. The liability insurance market. Journal of Economic Perspectives 5(3):115-36.

Winter, Ralph A. 1991b. Solvency regulation and the property-liability insurance cycle. Economic Inquiry 29:458-71.

Winter, Ralph A. 1994. The dynamics of competitive insurance markets. Journal of Financial Intermediation 3:379-415.

[Figure 1 omitted]
Table 1

Summary of Alternative Models' Implications for Economic Loss Ratio

                                          Variable
                                  Interest Rate           Surplus
Model                         SR               LR     SR

Actuarial                     +                +      +
Capacity Constraint (CCH)     +                +      +
Economic                      +                +      +
Financial                     +                +      0
Financial Quality (FQH)       +                +      +
Option Pricing (OP)        [+ or -]         [+ or -]  -

                                       Variable
                            Surplus           Variance
Model                      LR           SR          LR

Actuarial                  0            -           0
Capacity Constraint (CCH)  0            -           0
Economic                   +            -           -
Financial                  0            0           0
Financial Quality (FQH)    -         [+ or -]    [+ or -]
Option Pricing (OP)        -            +           +
Table 2

Augmented Dicky-Fuller Tests of the Unit Root Hypothesis, 1935-1997

A. No Time Trend (a)

Variable  Level  Difference   Log   Log Difference

ELR1      -3.36    -6.23     -3.34      -6.31
ELR2      -3.21    -6.51     -3.20      -6.65
ELR3      -3.24    -6.69     -3.54      -6.83
TBR       -1.57    -5.71     -1.53      -5.75
MGB       -1.27    -3.90     -1.13      -5.12
LGB       -1.02    -3.43     -0.90      -3.99
BCAP      -1.65    -5.48     -1.61      -5.36
BSA       -1.56    -4.99     -1.61      -5.36
RCAP      -3.19    -5.64     -3.17      -5.84

B. Time Trend (b)

Variable  Level  Difference   Log   Log Difference

ELR1      -4.23    -6.17     -4.17      -6.25
ELR2      -4.13    -6.47     -4.08      -6.61
ELR3      -4.37    -6.64     -4.31      -6.78
TBR       -2.21    -5.69     -2.08      -5.74
MGB       -1.34    -3.97     -0.97      -6.83
LGB       -1.54    -3.41     -1.21      -4.00
BCAP      -1.35    -5.57     -1.24      -5.46
BSA       -1.41    -5.04     -1.35      -4.97
RCAP      -3.70     5.59     -3.63      -5.78

(a)The critical values are -2.59, -2.91, and -3.54 for tests at the 10%,
5%, and 1% levels, respectively.

(b)The critical values are -3.17, -3.48, and -4.11 for tests at the 10%,
5%, and 1% levels, respectively.
Table 3

Estimates of Regression Models for the Economic Loss Ratio, 1935-1997

                               OLS Models
                      [y.sub.t] = [[alpha].sub.0] +
                           [[alpha].sub.1]t +
                       [[phi].sub.1][y.sub.t-1] +
                      [[phi].sub.t-2][y.sub.t-2] +
                    [[beta].sub.1][DELTA] [r.sub.t] +
                    [[beta].sub.2][DELTA][r.sub.t-1]
                            + [[gamma].sub.1]
                           [DELTA][S.sub.t] +
                    [[gamma].sub.2][DELTA][S.sub.t-1]
                          + [[epsilon].sub.t],
Variable                                        1

C                                            35.362
                                             (4.32)
D81                                              --

TIME                                          0.062
                                             (1.79)
ELR1(-1)                                      0.708
                                             (5.27)
ELR1(-2)                                     -0.184
                                             (1.52)
[DELTA]TBR                                   -1.252
                                             (3.39)
[DELTA]TBR(-1)                                0.058
                                             (0.14)
[DELTA]BCAP                                   5.424
                                             (1.18)
[DELTA]BCAP(-1)                               9.20
                                             (2.15)
[DELTA]BSA

[DELTA]BSA(-1)

RC

RC(-1)

Adjusted [R.sup.2]                            0.641
Period                                       10.470

                                 OLS Models
                       [y.sub.t] = [[alpha].sub.0] +
                             [[alpha].sub.1]t +
                         [[phi].sub.1][y.sub.t-1] +
                        [[phi].sub.t-2][y.sub.t-2] +
                     [[beta].sub.1][DELTA] [r.sub.t] +
                     [[beta].sub.2][DELTA][r.sub.t-1] +
                              [[gamma].sub.1]
                             [DELTA][S.sub.t] +
                    [[gamma].sub.2][DELTA][S.sub.t-1] +
                             [[epsilon].sub.t],
Variable                     2

C                         25.215
                          (2.63)
D81                         --

TIME                       0.229
                          (2.92)
ELR1(-1)                   0.656
                          (4.62)
ELR1(-2)                  -0.263
                          (2.19)
[DELTA]TBR                -1.258
                          (3.312)
[DELTA]TBR(-1)            -0.030
                          (0.66)
[DELTA]BCAP                 --

[DELTA]BCAP(-1)             --

[DELTA]BSA                23.494
                          (1.11)
[DELTA]BSA(-1)            30.366
                          (2.13)
RC

RC(-1)

Adjusted [R.sup.2]         0.635
Period                     7.166

                              OLS Models
                     [y.sub.t] = [[alpha].sub.0] +
                          [[alpha].sub.1]t +
                      [[phi].sub.1][y.sub.t-1] +
                     [[phi].sub.t-2][y.sub.t-2] +
                    [[beta].sub.1][DELTA] [r.sub.t]
                                   +
                    [[beta].sub.2][DELTA][r.sub.t-1
                          ] + [[gamma].sub.1]
                          [DELTA][S.sub.t] +
                    [[gamma].sub.2][DELTA][S.sub.t-
                        1] + [[epsilon].sub.t],
Variable                         3

C                             38.854
                              (3.76)
D81                             --

TIME                           0.095
                              (2.52)
ELR1(-1)                       0.630
                              (4.81)
ELR1(-2)                      -0.157
                              (1.34)
[DELTA]TBR                    -1.498
                              (3.97)
[DELTA]TBR(-1)                -0.137
                              (0.36)
[DELTA]BCAP

[DELTA]BCAP(-1)

[DELTA]BSA

[DELTA]BSA(-1)

RC                           -59.666
                              (3.28)
RC(-1)                        54.900
                              (2.84)
Adjusted [R.sup.2]             0.675
Period                         9.640

                                 OLS Models
                       [y.sub.t] = [[alpha].sub.0] +
                             [[alpha].sub.1]t +
                         [[phi].sub.1][y.sub.t-1] +
                        [[phi].sub.t-2][y.sub.t-2] +
                     [[beta].sub.1][DELTA] [r.sub.t] +
                     [[beta].sub.2][DELTA][r.sub.t-1] +
                              [[gamma].sub.1]
                             [DELTA][S.sub.t] +
                    [[gamma].sub.2][DELTA][S.sub.t-1] +
                             [[epsilon].sub.t],
Variable              4

C                   47.359
                    (5.58)
D81                  4.873
                    (3.43)
TIME                  --

ELR1(-1)             0.662
                    (5.04)
ELR1(-2)            -0.258
                    (2.22)
[DELTA]TBR          -0.725
                    (1.98)
[DELTA]TBR(-1)

[DELTA]BCAP

[DELTA]BCAP(-1)

[DELTA]BSA

[DELTA]BSA(-1)

RC

RC(-1)

Adjusted [R.sup.2]   0.650
Period               7.297

                                 OLS Models
                        [y.sub.t] = [[alpha].sub.0] +
                             [[alpha].sub.1]t +
                         [[phi].sub.1][y.sub.t-1] +
                        [[phi].sub.t-2][y.sub.t-2] +
                      [[beta].sub.1][DELTA] [r.sub.t] +
                     [[beta].sub.2][DELTA][r.sub.t-1] +
                               [[gamma].sub.1]
                             [DELTA][S.sub.t] +
                     [[gamma].sub.2][DELTA][S.sub.t-1] +
                             [[epsilon].sub.t],
Variable              5

C                   45.349
                    (5.37)
D81                  4.324
                    (3.00)
TIME                  --

ELR1(-1)             0.666
                    (5.51)
ELR1(-2)            -0.235
                    (2.02)
[DELTA]TBR          -0.875
                    (2.35)
[DELTA]TBR(-1)

[DELTA]BCAP

[DELTA]BCAP(-1)

[DELTA]BSA

[DELTA]BSA(-1)
                    29.715
RC                  (1.63)

RC(-1)

Adjusted [R.sup.2]   0.660
Period               7.723

                              OLS Models
                     [y.sub.t] = [[alpha].sub.0] +
                          [[alpha].sub.1]t +
                      [[phi].sub.1][y.sub.t-1] +
                     [[phi].sub.t-2][y.sub.t-2] +
                    [[beta].sub.1][DELTA] [r.sub.t]
                                   +
                    [[beta].sub.2][DELTA][r.sub.t-1
                          ] + [[gamma].sub.1]
                          [DELTA][S.sub.t] +
                    [[gamma].sub.2][DELTA][S.sub.t-
                        1] + [[epsilon].sub.t],
Variable                                     6

C                                          40.711
                                           (3.79)
D81                                         3.872
                                           (2.41)
TIME                                         --

ELR1(-1)                                    0.505
                                           (4.92)
ELR1(-2)                                     --

[DELTA]TBR                                 -1.217
                                           (3.02)
[DELTA]TBR(-1)                               --

[DELTA]BCAP

[DELTA]BCAP(-1)

[DELTA]BSA

[DELTA]BSA(-1)

RC
                                          -53.757
RC(-1)                                     (3.25)
                                           48.701
Adjusted [R.sup.2]                         (2.47)
Period                                      0.681

[y.sub.t] is the economic loss ratio ELR1, t is a linear
time trend, [r.sub.t] is the T-bill rate (TBR), and [S.sub.t]
is the ratio of surplus to premiums (BCAP), the ratio of
surplus to assets (BSA), or the ratio of surplus to a lagged moving
average (RC). D81 is a dummy variable taking the value 1 after 1981.
Absolute value of coefficient to estimated standard error in
parentheses.
Table 4

Estimates of ARCH-M Regression Models for the Economic Loss
Ratio, 1935-1997

                                EGARCH-M Models
                         [y.sub.t] = [[alpha].sub.0] +
                               [[alpha].sub.1]t +
                           [[phi].sub.1][y.sub.t-1] +
                           [[phi].sub.2][y.sub.t-2] +
                        [[beta.sub.1][DELTA][r.sub.t] +
                       [[beta].sub.2][DELTA][r.sub.t-1] +
                             [[gamma].sub.1][DELTA]
                                  [S.sub.t] +
                       [[gamma].sub.2][DELTA][S.sub.t-1]
                            + [delta][square root of
                                  ([h.sub.t])
                       +[[epsilon].sub.t],[[zeta].sub.t-
                                     1] -
                        ln [h.sub.t] = [[theta].sub.0] +
                       [[theta].sub.1][\[[zeta].sub.t-1]                         - [square root of((2/[pi]))]]
                        +[[theta].sub.2][[zeta].sub.t-1]
                         +[[theta].sub.3]ln [h.sub.t-1]
                          1
Variable                ELR1

Constant                36.023
                       (17.3)
D66                      --

D81                      --

TIME                     0.019
                        (1.96)
[ELR.sub.t-1]            0.904
                       (53.76)
[ELR.sub.t-2]           -0.145
                        (5.18)
[DELTA][r.sub.t]        -1.528
                        (4.25)
[DELTA][BACP.sup.t-1]   16.895
                        (4.31)
CV                      -5.976
                       (16.29)
[[theta].sub.0]          1.054
                        (9.88)
[[theta].sub.1]          0.024
                        (7.77)
[[theta].sub.2]          0.183
                        (4.93a)
[[theta].sub.3]          0.491
                       (33.38)
D50                      --

D66                      --

TIME                     --

Adjusted [R.sup.2]       0.701
Period                   --

                                  EGARCH-M Models
                           [y.sub.t] = [[alpha].sub.0] +
                                [[alpha].sub.1]t +
                            [[phi].sub.1][y.sub.t-1] +
                            [[phi].sub.2][y.sub.t-2] +
                          [[beta.sub.1][DELTA][r.sub.t] +
                        [[beta].sub.2][DELTA][r.sub.t-1] +
                              [[gamma].sub.1][DELTA]
                                    [S.sub.t] +
                        [[gamma].sub.2][DELTA][S.sub.t-1] +
                        [delta][square root of ([h.sub.t])
                       +[[epsilon].sub.t],[[zeta].sub.t-1]
                                         -
                         ln [h.sub.t] = [[theta].sub.0] +
                       [[theta].sub.1][\[[zeta].sub.t-1]\ -
                            [square root of((2/[pi]))]]
                         +[[theta].sub.2][[zeta].sub.t-1]
                          +[[theta].sub.3]ln [h.sub.t-1]
                           2
Variable                  ELR2

Constant                 37.502
                         (5.43)
D66                          --

D81                          --

TIME                      0.044
                         (1.25)
[ELR.sub.t-1]             0.946
                         (8.65)
[ELR.sub.t-2]            -0.226
                         (1.28)
[DELTA][r.sub.t]         -1.504
                         (2.57)
[DELTA][BACP.sup.t-1]    17.076
                         (2.70)
CV                       -5.640
                         (4.37)
[[theta].sub.0]           1.271
                         (1.72)
[[theta].sub.1]           0.072
                         (2.08)
[[theta].sub.2]           0.300
                       (248.28)
[[theta].sub.3]           0.344
                         (1.04)
D50                       0.152
                         (1.52)
D66                          --

TIME                         --

Adjusted [R.sup.2]        0.703
Period                       --

                                 EGARCH-M Models
                          [y.sub.t] = [[alpha].sub.0] +
                                [[alpha].sub.1]t +
                            [[phi].sub.1][y.sub.t-1] +
                            [[phi].sub.2][y.sub.t-2] +
                         [[beta.sub.1][DELTA][r.sub.t] +
                        [[beta].sub.2][DELTA][r.sub.t-1] +
                              [[gamma].sub.1][DELTA]
                                   [S.sub.t] +
                       [[gamma].sub.2][DELTA][S.sub.t-1] +
                        [delta][square root of ([h.sub.t])
                       +[[epsilon].sub.t],[[zeta].sub.t-1]
                                        -
                         ln [h.sub.t] = [[theta].sub.0] +
                       [[theta].sub.1][\[[zeta].sub.t-1]\ -
                           [square root of((2/[pi]))]]
                         +[[theta].sub.2][[zeta].sub.t-1]
                          +[[theta].sub.3]ln [h.sub.t-1]
                          3
Variable                 ELR3

Constant                 39.106
                        (23.71)
D66                       --

D81                       --

TIME                      0.203
                        (16.69)
[ELR.sub.t-1]             0.824
                       (134.58)
[ELR.sub.t-2]            -0.243
                        883.19)
[DELTA][r.sub.t]         -1.931
                         (2.33)
[DELTA][BACP.sup.t-1]    14.419
                        (10.47)
CV                       -6.587
                       (315.33)
[[theta].sub.0]           0.383
                         (2.72)
[[theta].sub.1]           0.052
                         (1.00)
[[theta].sub.2]           0.187
                         (5.00a)
[[theta].sub.3]           0.514
                       (201.27)
D50                       --

D66                       --

TIME                      --

Adjusted [R.sup.2]        0.654
Period                   10.810

                                 EGARCH-M Models
                          [y.sub.t] = [[alpha].sub.0] +
                                [[alpha].sub.1]t +
                            [[phi].sub.1][y.sub.t-1] +
                            [[phi].sub.2][y.sub.t-2] +
                         [[beta.sub.1][DELTA][r.sub.t] +
                        [[beta].sub.2][DELTA][r.sub.t-1] +
                              [[gamma].sub.1][DELTA]
                                   [S.sub.t] +
                       [[gamma].sub.2][DELTA][S.sub.t-1] +
                        [delta][square root of ([h.sub.t])
                       +[[epsilon].sub.t],[[zeta].sub.t-1]
                                        -
                         ln [h.sub.t] = [[theta].sub.0] +
                       [[theta].sub.1][\[[zeta].sub.t-1]\ -
                           [square root of((2/[pi]))]]
                         +[[theta].sub.2][[zeta].sub.t-1]
                          +[[theta].sub.3]ln [h.sub.t-1]
                          4
Variable               LNELR1

Constant                 2.204
                       (73.91)
D66                     -0.023
                        (2.50)
D81                      --

TIME                     0.001
                        (4.86)
[ELR.sub.t-1]            0.804
                        (1.58a)
[ELR.sub.t-2]           -0.211
                        (11.88)
[DELTA][r.sub.t]        -2.306
                        (3.78)
[DELTA][BACP.sup.t-1]    0.158
                        (3.12)
CV                      -13.741
                        (10.02)
[[theta].sub.0]          -4.967
                         (8.05)
[[theta].sub.1]           0.011
                         (0.32)
[[theta].sub.2]           0.128
                        (82.05)
[[theta].sub.3]           0.2660
                         (2.70)
D50                      --

D66                      --

TIME                     --

Adjusted [R.sup.2]        0.775
Period                   12.441

                                 EGARCH-M Models
                          [y.sub.t] = [[alpha].sub.0] +
                               [[alpha].sub.1]t +
                           [[phi].sub.1][y.sub.t-1] +
                           [[phi].sub.2][y.sub.t-2] +
                         [[beta.sub.1][DELTA][r.sub.t] +
                       [[beta].sub.2][DELTA][r.sub.t-1] +
                             [[gamma].sub.1][DELTA]
                                   [S.sub.t] +
                       [[gamma].sub.2][DELTA][S.sub.t-1] +
                       [delta][square root of ([h.sub.t])
                       +[[epsilon].sub.t],[[zeta].sub.t-1
                                      ] -
                        ln [h.sub.t] = [[theta].sub.0] +
                       [[theta].sub.1][\[[zeta].sub.t-1]                          - [square root of((2/[pi]))]]
                        +[[theta].sub.2][[zeta].sub.t-1]
                         +[[theta].sub.3]ln [h.sub.t-1]
                          5
Variable               LNELR2

Constant                2.480
                       (5.49)
D66                      --

D81                     0.042
                       (2.82)
TIME                    0.001
                       (4.00)
[ELR.sub.t-1]           0.773
                       (4.94)
[ELR.sub.t-2]          -0.332
                       (2.58)
[DELTA][r.sub.t]         --

[DELTA][BACP.sup.t-1]   0.099
                       (1.91)
CV                     -3.15
                       (4.00)
[[theta].sub.0]        -2.953
                       (3.47)
[[theta].sub.1]         0.104
                       (0.874)
[[theta].sub.2]         0.337
                       (9.24)
[[theta].sub.3]         0.596
                       (4.78)
D50                      --

D66                     0.285
                       (2.50)
TIME                     --

Adjusted [R.sup.2]      0.739
Period                  9.367

                               EGARCH-M Models
                        [y.sub.t] = [[alpha].sub.0] +
                             [[alpha].sub.1]t +
                         [[phi].sub.1][y.sub.t-1] +
                         [[phi].sub.2][y.sub.t-2] +
                       [[beta.sub.1][DELTA][r.sub.t] +
                       [[beta].sub.2][DELTA][r.sub.t-1
                         ] + [[gamma].sub.1][DELTA]
                                 [S.sub.t] +
                       [[gamma].sub.2][DELTA][S.sub.t-
                         1] + [delta][square root of
                                 ([h.sub.t])
                       +[[epsilon].sub.t],[[zeta].sub
                                  .t-1] -
                       ln [h.sub.t] = [[theta].sub.0]
                                      +
                       [[theta].sub.1][\[[zeta].sub.t-
                             1]\ - [square root
                               of((2/[pi]))]]
                       +[[theta].sub.2][[zeta].sub.t-1
                            ] +[[theta].sub.3]ln
                                 [h.sub.t-1]
                          6
Variable               LNELR3

Constant                 2.067
                        (6.41)
D66                      --

D81                      --

TIME                     0.003
                        (2.42)
[ELR.sub.t-1]            0.882
                       (13.80)
[ELR.sub.t-2]           -0.298
                        (2.95)
[DELTA][r.sub.t]         --

[DELTA][BACP.sup.t-1]    0.158
                        (3.37)
CV                     -12.190
                        (3.17)
[[theta].sub.0]         -4.421
                        (8.11)
[[theta].sub.1]          0.056
                        (1.34)
[[theta].sub.2]          0.058
                        (2.30)
[[theta].sub.3]          0.406
                        (8.03)
D50                      --

D66                      --

TIME                     0.006
                        (1.55)
Adjusted [R.sup.2]       0.713
Period                   9.969

[y.sub.t] is the economic loss ratio ELR, TIME is a linear time trend,
[r.sub.t] is the corresponding interest rate, [S.sub.t] is the ratio of
surplus to premiums (BCAP), and [h.sub.t] is the conditinal variance
(CV). D50, D66, and D81 are dummy variable staking the value 1 after
1950, 1966, and 1981. Absolute value of coefficient to estimated
standard error in parentheses. The "a" indicates times E + 100.
Table 5

Estimates of ARCH-M Regression Models for the Economic Loss Ratio,
1935-1997

EGARCH-M Modles

[y.sub.t] = [[alpha].sub.0] + [[alpha].sub.1]t + [[phi].sub.1]
[y.sub.t-1] + [[phi].sub.2] [y.sub.t-2] + [[beta].sub.1]
[DELTA][r.sub.t] + [[beta].sub.2] [DELTA][r.sub.t-1] +
[[gamma].sub.t][DELTA][S.sub.t-1] + [delta][square root of
([h.sub.t])] + [[epsilon].sub.t], ln [h.sub.t] = [[theta].sub.0] +
[[theta].sub.1][\[[zeta].sub.t-1]\ - [square root of (2/[pi])]] +
[[theta].sub.2][[zeta].sub.t-1] + [[theta].sub.3]ln [h.sub.t-1]

                        1        2         3        4          5
Variable               ELR1     ELR2     ELR3     LNELR1    LNELR2

Constant              42.755    38.139   36.738     2.436      2.607
                      (3.53)  (275.26)   (7.17)  (231.64)   (256.29)
D81                    3.928     2.801    1.708     0.032      0.062
                      (1.58)    (6.87)   (0.95)    (1.83)     (3.45)
TIME                   0.114     --       0.038     0.002     -0.000
                      (3.73)             (1.34)    (6.77)     (0.66)
[ELR.sub.t-1]          0.706     0.959    0.940     0.724      0.787
                      (7.40)   (82.27)  (34.25)  (520.55)  (5123.31)
[ELR.sub.t-2]         -0.238    -0.258   -0.273    -0.267  -0372
                      (2.18)   (84.78)   (3.20)   (10.08)    (38.19)
[DELTA][r.sub.t]      -1.159     --      -0.189    -1.470      0.001
                      (2.22)             (0.20)    (1.97)     (0.04)
[DELTA][BSA.sub.t-1]  42.967    52.859   49.43      0.185      0.104
                      (2.30)    (3.86)   (2.88)    (1.71)     (1.40)
CV                    -2.112    -5.214   -4.815    -4.034     -0.849
                      (1.82)   (34.14)   (2.92)    (1.22)     (1.73)
[[phi].sub.0]          0.481     0.664    0.669    -3.918    -12.313
                      (1.01)    (5.84)   (1.98)    (3.27)     (6.48)
[[theta].sub.1]        0.055     0.081    0.079     0.067      0.515
                      (0.36)    (4.85)   (1.67)    (0.60)     (1.43)
[[theta].sub.2]        0.468     0.158    0.229     0.342      0.115
                      (1.64)    (5.69)   (2.41)     (147)     (0.57)
[[theta].sub.3]        0.658     0.646    0.631     0.450     -0.991
                      (3.10)   (16.29)   (4.19)    (2.38)     (4.60)
D50                              --                           -1.667
                                                              (1.24)
D66                    0.463     --                 0.329
                      (1.68)                       (1.33)
Adjusted [R.sup.2]     0.746     0.679    0.698     0.745      0.545
Period                 8.24     18.69    13.90      7.91       7.22

                          6
Variable               LNELR3

Constant                  2.465
                       (291.33)
D81                       0.026
                         (1.32)
TIME                      0.002
                         (4.45)
[ELR.sub.t-1]             0.818
                      (2797.62)
[ELR.sub.t-2]            -0.378
                        (27.55)
[DELTA][r.sub.t]          0.021
                         (0.21)
[DELTA][BSA.sub.t-1]      0.153
                         (1.68)
CV                       -3.689
                         (2.34)
[[phi].sub.0]            -3.250
                         (2.48)
[[theta].sub.1]           0.085
                         (1.01)
[[theta].sub.2]           0.320
                         (2.40)
[[theta].sub.3]           0.550
                         (3.02)
D50

D66                       0.303
                         (1.84)
Adjusted [R.sup.2]        0.729
Period                    7.45

[y.sub.t] is the economic loss ratio ELR, t is a linear time trend,
[r.sub.t] is the corresponding interest rate, [S.sub.t] is the ratio of
surplus to assets (BSA), and [h.sub.t] is the conditional variance (CV).
D50, D66, and D81 are dummy variables taking the value 1 after 1950,
1966, and 1981. Absolute value of coefficient to estimated standard
error in parentheses; the "a" indicates times E + 100.
Table 6

Estimates of ARCH-M Regression Models for the Economic Loss Ratio,
1935-1997

                           EGARCH-M Models
                    [y.sub.t] = [[alpha].sub.0] +
                         [[alpha].sub.1]t +
                     [[phi].sub.1][y.sub.t-1] +
                     [[phi].sub.2][y.sub.t-2] +
                    [[beta].sub.1][DELTA][r.sub.t
                                 ] +
                    [[beta].sub.2][DELTA][r.sub.t
                                -1] +
                    [[gamma].sub.t][DELTA][S.sub.
                    t-1] + [delta][square rootof
                           ([h.su.t])], +
                        [[epsilon].sub.t], ln
                    [h.sub.t], =[[theta].sub.0] +
                    [[theta].sub.t][\[[zeta].sub.
                               t-1]\ -
                    [square root of ((2/[pi]))]]
                          + [[theta].sub.2]
                         [[zeta].sub.t-1] +
                         [[theta].sub.3],ln
                             [h.sub.t-1]
                       1
Variable              ELR1

Constant             34.766
                     (3.10)
D81                   4.635
                     (2.01)
TIME                  0.077
                     (1.99)
[ELR.sub.t-1]         0.739
                    (91.95)
[ELR.sub.t-2]        -0.230
                     (2.18)
[DELTA][r.sub.t]     -0.880
                     (1.48)
[RC.sub.t-1]         29.608
                     (1.55)
CV                   -2.450
                     (1.44)
[[phi].sub.0]         0.366
                     (0.98)
[[theta].sub.1]       0.133
                     (1.03)
[[theta].sub.2]       0.386
                     (1.32)
[[theta].sub.3]       0.717
                     (4.14)
D50                   --

D66                   0.334
                     (1.19)
Adjusted [R.sup.2]    0.734
Period                9.08

                            EGARCH-M Models
                     [y.sub.t] = [[alpha].sub.0] +
                           [[alpha].sub.1]t +
                       [[phi].sub.1][y.sub.t-1] +
                       [[phi].sub.2][y.sub.t-2] +
                    [[beta].sub.1][DELTA][r.sub.t] +
                    [[beta].sub.2][DELTA][r.sub.t-1]
                                   +
                    [[gamma].sub.t][DELTA][S.sub.t-1
                       ] + [delta][square rootof
                             ([h.su.t])], +
                    [[epsilon].sub.t], ln [h.sub.t],
                           =[[theta].sub.0] +
                    [[theta].sub.t][\[[zeta].sub.t-1
                                  ]\ -
                     [square root of ((2/[pi]))]] +
                    [[theta].sub.2] [[zeta].sub.t-1]
                    + [[theta].sub.3],ln [h.sub.t-1]
                        2
Variable              ELR2

Constant             91.767
                     (2.39)
D81                   2.566
                     (1.61)
TIME                  --

[ELR.sub.t-1]         0.834
                     (5.35)
[ELR.sub.t-2]        -0.347
                     (2.59)
[DELTA][r.sub.t]     -0.320
                     (0.58)
[RC.sub.t-1]         25.972
                     (1.84)
CV                  -20.464
                     (1.60)
[[phi].sub.0]         3.218
                     (3.67)
[[theta].sub.1]       0.062
                     (1.71)
[[theta].sub.2]       0.011
                     (0.55)
[[theta].sub.3]      -0.578
                     (1.83)
D50                   --

D66                  -0.027
                     (0.55)
Adjusted [R.sup.2]    0.672
Period                8.01

                            EGARCH-M Models
                     [y.sub.t] = [[alpha].sub.0] +
                           [[alpha].sub.1]t +
                       [[phi].sub.1][y.sub.t-1] +
                       [[phi].sub.2][y.sub.t-2] +
                    [[beta].sub.1][DELTA][r.sub.t] +
                    [[beta].sub.2][DELTA][r.sub.t-1]
                                   +
                    [[gamma].sub.t][DELTA][S.sub.t-1
                       ] + [delta][square rootof
                             ([h.su.t])], +
                    [[epsilon].sub.t], ln [h.sub.t],
                           =[[theta].sub.0] +
                    [[theta].sub.t][\[[zeta].sub.t-1
                                  ]\ -
                     [square root of ((2/[pi]))]] +
                    [[theta].sub.2] [[zeta].sub.t-1]
                    + [[theta].sub.3],ln [h.sub.t-1]
                         3
Variable                ELR3

Constant               38.676
                     (947.44)
D81                     2.319
                       (0.97)
TIME                    0.131
                       (4.32)
[ELR.sub.t-1]           0.783
                    (1858.10)
[ELR.sub.t-2]          -0.304
                      (33.78)
[DELTA][r.sub.t]       -0.306
                       (0.20)
[RC.sub.t-1]           16.392
                       (1.22)
CV                     -3.327
                       (3.04)
[[phi].sub.0]           0.602
                       (1.59)
[[theta].sub.1]         0.122
                      (12.80)
[[theta].sub.2]         0.362
                       (2.64)
[[theta].sub.3]         0.602
                       (3.73)
D50                     --

D66                     0.324
                       (1.74)
Adjusted [R.sup.2]      0.740
Period                  8.04

                            EGARCH-M Models
                     [y.sub.t] = [[alpha].sub.0] +
                           [[alpha].sub.1]t +
                       [[phi].sub.1][y.sub.t-1] +
                       [[phi].sub.2][y.sub.t-2] +
                    [[beta].sub.1][DELTA][r.sub.t] +
                    [[beta].sub.2][DELTA][r.sub.t-1]
                                   +
                    [[gamma].sub.t][DELTA][S.sub.t-1
                       ] + [delta][square rootof
                             ([h.su.t])], +
                    [[epsilon].sub.t], ln [h.sub.t],
                           =[[theta].sub.0] +
                    [[theta].sub.t][\[[zeta].sub.t-1
                                  ]\ -
                     [square root of ((2/[pi]))]] +
                    [[theta].sub.2] [[zeta].sub.t-1]
                    + [[theta].sub.3],ln [h.sub.t-1]
                         4
Variable               LNELR1

Constant                2.327
                      (25.200)
D81                     0.049
                       (2.32)
TIME                    --

[ELR.sub.t-1]           0.737
                    (2290.77)
[ELR.sub.t-2]          -0.170
                       (4.64)
[DELTA][r.sub.t]       -0.968
                       (1.66)
[RC.sub.t-1]            0.116
                       (2.41)
CV                     -8.122
                       (2.06)
[[phi].sub.0]          -1.768
                       (4.25)
[[theta].sub.1]         0063
                       (2.46)
[[theta].sub.2]         0.105
                       (1.82)
[[theta].sub.3]         0.748
                      (12.98)
D50                     --

D66                     0.027
                       (1.18)
Adjusted [R.sup.2]      0.751
Period                 13.51

                            EGARCH-M Models
                     [y.sub.t] = [[alpha].sub.0] +
                           [[alpha].sub.1]t +
                       [[phi].sub.1][y.sub.t-1] +
                       [[phi].sub.2][y.sub.t-2] +
                    [[beta].sub.1][DELTA][r.sub.t] +
                    [[beta].sub.2][DELTA][r.sub.t-1]
                                   +
                    [[gamma].sub.t][DELTA][S.sub.t-1
                       ] + [delta][square rootof
                             ([h.su.t])], +
                    [[epsilon].sub.t], ln [h.sub.t],
                           =[[theta].sub.0] +
                    [[theta].sub.t][\[[zeta].sub.t-1
                                  ]\ -
                     [square root of ((2/[pi]))]] +
                    [[theta].sub.2] [[zeta].sub.t-1]
                    + [[theta].sub.3],ln [h.sub.t-1]
                        5
Variable             LNELR2

Constant              2.262
                     (3.57)
D81                   0.072
                     (2.44)
TIME                 -0.001
                     (1.11)
[ELR.sub.t-1]         0.791
                     (7.30)
[ELR.sub.t-2]        -0.310
                     (2.68)
[DELTA][r.sub.t]      --

[RC.sub.t-1]          0.058
                     (0.93)
CV                   -6.20
                     (1.75)
[[phi].sub.0]       -12.613
                    (19.95)
[[theta].sub.1]       0.050
                     (1.26)
[[theta].sub.2]      -0.049
                     (0.81)
[[theta].sub.3]      -0.976
                    (12.40)
D50                  -0.609
                     (1.69)
D66                   --

Adjusted [R.sup.2]    0.725
Period                8.04

                          EGARCH-M Models
                    [y.sub.t] = [[alpha].sub.0]
                       + [[alpha].sub.1]t +
                    [[phi].sub.1][y.sub.t-1] +
                    [[phi].sub.2][y.sub.t-2] +
                    [[beta].sub.1][DELTA][r.sub
                               .t] +
                    [[beta].sub.2][DELTA][r.sub
                              .t-1] +
                    [[gamma].sub.t][DELTA][S.su
                      b.t-1] + [delta][square
                       rootof ([h.su.t])], +
                       [[epsilon].sub.t], ln
                    [h.sub.t], =[[theta].sub.0]
                                 +
                    [[theta].sub.t][\[[zeta].su
                             b.t-1]\ -
                          [square root of
                          ((2/[pi]))]] +
                          [[theta].sub.2]
                        [[zeta].sub.t-1] +
                        [[theta].sub.3],ln
                            [h.sub.t-1]
                         6
Variable               LNELR3

Constant                2.443
                     (205.69)
D81                     0.023
                       (1.48)
TIME                    0.001
                       (3.97)
[ELR.sub.t-1]           0.818
                    (4153.25)
[ELR.sub.t-2]          -0.310
                      (29.75)
[DELTA][r.sub.t]        --

[RC.sub.t-1]            0.041
                       (0.82)
CV                     -9.655
                       (3.95)
[[phi].sub.0]          -3.300
                      (10.89)
[[theta].sub.1]         0.061
                       (2.22)
[[theta].sub.2]         0.128
                       (3.05)
[[theta].sub.3]         0.528
                       (8.82)
D50                     --

D66                     0.110
                       (4.78)
Adjusted [R.sup.2]      0.747
Period                  8.43

[y.sub.t] is the economic loss ratio ELR, t is a linear time trend,
[r.sub.t] is the corresponding interest rate, [S.sub.t] the ratio of
surplus to a moving average (RC) [h.sub.t] is the conditional variance
(CV). D50,  D66, and D81 are dummy variables taking the value 1 after
1950, 1966, and  1981. Absolute value of coefficient to estimated
standard error in parentheses; the "a" indicates times E + 100.
COPYRIGHT 2002 Southern Economic Association
No portion of this article can be reproduced without the express written permission from the copyright holder.
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Author:Thistle, Paul D.
Publication:Southern Economic Journal
Geographic Code:1USA
Date:Jan 1, 2002
Words:13398
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