Basis risk with PCS catastrophe insurance derivative contracts.
The increased frequency and severity of insured losses from natural catastrophes in the past decade have generated a variety of industry responses, including "Act of God" bonds, new catastrophe reinsurers in Bermuda, the introduction of a catastrophe insurance risk exchange, and the development of catastrophe insurance derivatives (see ISO 1996). Catastrophe derivatives trade at the Chicago Board of Trade (CBOT) and have been introduced by the Bermuda commodities exchange (Greenwald 1997). These contracts have payoffs that depend on indices that measure insured losses from catastrophes in specific geographical regions. In principle, insurers can use these contracts to reduce their exposure to underwriting losses due to catastrophes.
Hedging using index-based derivatives has the potential to reduce contracting costs, moral hazard, and tax costs relative to other methods of managing underwriting risk, such as reinsurance and equity capital (e.g., Niehaus and Mann 1992; Harrington, Mann, and Niehaus 1995). A potential shortcoming is that index-based derivative contracts can have considerable basis risk, i.e., the losses on a particular insurer's book of business may not be highly correlated with the indices underlying the contracts so that little underwriting risk can be eliminated. Since the amount of basis risk is fundamental to the success of derivative contracts (Black 1986), the main purpose of this paper is to examine the basis risk of catastrophe insurance derivative contracts.
The first insurance derivative contracts, introduced by the CBOT in December 1992, were futures contracts based on the underwriting results of twenty-two insurers for the entire country and for three regions of the United States (east, mid-west, and west). Trading volume for these contracts was anemic during their two years of existence, which has been attributed to several factors, including: (1) insurers/reinsurers' preference for option spreads (a long call position combined with a short call position with a higher exercise price) as opposed to futures contracts because option spreads have payoffs similar to catastrophe reinsurance contracts (Cummins and Geman 1994; 1995); (2) the lack of historical data on the underlying indices (D'Arcy and France 1992); and (3) the basis risk associated with broad geographical indices. The CBOT discontinued the futures contracts and now only trades option contracts based on Property Claims Service's (PCS) estimates of catastrophe losses in specific geographical regions. The new contracts were designed to address many of the concerns of the original contracts. First, a wide variety of call spreads can be created and prices are quoted on pre-packaged call spreads. Second, data on the underlying indices are available beginning in 1949. Third, PCS option contracts are available on nine indices: a national index, five regional indices (Eastern, Northeastern, Southeastern, Midwestern and Western), and three individual state indices (California, Florida and Texas). While trading volume in these second-generation contracts is greater than the first generation contracts, overall volume has still been relatively low. It is worth noting, however, that some very successful derivative contracts (e.g., the T-bond futures contract) had low volume when first introduced.
Although the more narrowly defined geographical areas should help reduce basis risk, industry analysts continue to cite basis risk as one of the main shortcomings of the PCS catastrophe options (Major 1997; Himick 1997). Critics contend that regional and even state-specific contracts cover too broad a geographical area and that contracts based on counties or zip codes might be better. A larger menu of disaggregated contracts (e.g., contracts for each zip code in Florida versus one Florida contract) can enhance an insurer's ability to combine contracts to form a portfolio of derivative contracts that is highly correlated with the insurer's book of business. However, greater disaggregation is not necessarily efficient because liquidity in any specific contract could decrease and the transactions costs associated with forming and monitoring an option portfolio could increase.
While we would ideally like to compare the benefits of lower basis risk to the additional liquidity and transaction costs associated with more disaggregated contracts, the appropriate method for making such a comparison is far from obvious. Thus, the objectives of this study are more modest. First, we provide evidence of whether state-specific catastrophe contracts based on PCS losses would likely be effective in hedging insurer underwriting risk. Evidence that state~specific contracts would provide reasonable hedging effectiveness would imply that basis risk should not be a major impediment to their success. Second, we examine the relative hedging effectiveness of state-specific PCS contracts compared with regional contracts based on PCS losses and with contracts based on state-specific industry losses within a line of business.
There are two general methods for examining basis risk associated with catastrophe derivative contracts. One approach, which is used by Major (1996), is to construct a computer simulation model to examine the correlation between a catastrophe loss index and an insurer's book of business. Given assumptions about the geographical distribution of insured property for a particular insurer and for the property underlying the catastrophe index, a sample correlation (or R-squared) between an insurer's losses and the index is calculated with simulated data for a large number of trials. based on this approach, Major (1996) suggests that indices based on catastrophe losses by zip code are likely to provide better hedges than statewide catastrophe indices.
The alternative approach, which we employ in this paper, is to provide evidence of basis risk by analyzing the historical correlation between catastrophe losses and individual insurer losses.(1) The use of historical data has some disadvantages (see the discussion below), and in practice insurer hedging decisions may rely primarily on simulation analysis. The analysis of historical data nonetheless is likely to provide additional insights concerning the magnitude of basis risk. For example, to the extent that there is stability over time in the probability distributions of insurer claim costs and catastrophe losses, evidence on the potential effectiveness of catastrophe derivatives in hedging underwriting risk in the past will provide an indication of their potential effectiveness in the future. Moreover, the results of computer simulation models are contingent on the assumptions built into the model.(2) Ultimately, these assumptions must be based, at least to some extent, on historical data. Thus, additional evidence concerning the historical relationship between catastrophe losses and insurer losses can provide further insight into the magnitude of basis risk and possibly enhance the implementation of computer simulation models.
Our empirical analysis utilizes a measure of potential hedging effectiveness that is common in hedging applications: the coefficient of determination (R-squared) between the variable to be hedged and the underlying index of the derivative contract. In particular, we estimate the R-squared between individual insurer groups' state-specific loss ratios, which reflect catastrophe and noncatastrophe losses, and state level catastrophe loss ratios using annual data over the 1974 through 1994 period. High R-squareds would imply that derivative contracts based on state-specific PCS catastrophe losses would be likely to provide effective hedges of by line and by state underwriting risk. An implication would be that portfolios of these contracts could be useful in constructing effective hedges against catastrophe-induced variation in aggregate loss ratios across multiple lines and states, thus helping to achieve more general risk reduction.3
One shortcoming of historical data analysis is that catastrophe loss distributions are highly skewed and have few, if any, observations from the tail of the catastrophe loss distribution during a given period. To examine the implications of a dearth of observations from the tail of the catastrophe loss distribution for sample R-squareds, we conduct a simple simulation analysis. The results of the simulation indicate that (a) sample R-squareds are likely to underestimate the population R-squareds in states with low realizations of catastrophes during the sample period, and (b) sample R-squareds are unlikely to substantially over-estimate the population R-squareds in states with high realizations of catastrophe derivatives during the sample period. Thus, the acid test of hedging effectiveness is whether insurers operating in states with a relatively high frequency or severity of catastrophes during the sample period typically have "high" R-squareds.
The first part of our empirical analysis relates individual insurer groups' loss ratios for homeowners, commercial multiple peril, and fire insurance in twenty eastern and southern states to state-specific catastrophe loss ratios using PCS catastrophe losses. In states that experienced significant catastrophe losses during the sample period, the evidence indicates that insurers' loss ratios generally are highly correlated with state catastrophe loss ratios. For example, in the four states with the highest mean catastrophe loss ratio during the sample period, the median R-squared for homeowners insurers in each state exceeds fifty percent, and the median R-squared exceeds seventy five percent in three of these states. Moreover, the median R-squared exceeds ninety percent in the two sample states (Florida and South Carolina) with the most severe catastrophes. These results suggest that state-specific PCS catastrophe options would provide effective hedges against variation in by line and state loss ratios caused by natural disasters. This finding in turn implies that these contracts could be used to construct effective hedges against catastrophe-induced variation in aggregate underwriting results across multiple lines and states.
As noted above, the CBOT originally proposed insurance derivatives based on line-specific loss ratios. Although this idea was replaced by catastrophe derivatives based on indices of aggregate (multiline) losses, it nonetheless is of interest to compare the hedging effectiveness of the PCS catastrophe contracts with line-specific contracts based on industry results because industry loss ratios will capture sources of correlation in losses other than catastrophes. If other sources of correlation are important, the use of industry loss ratios potentially could result in more effective hedges for insurers. The second component of our empirical analysis relates insurer loss ratios by line and state to industry statewide loss ratios for each line of business. We find that the statewide industry loss ratios, on average, would have provided more effective hedges than state-specific catastrophe loss ratios for each line. This does not imply that by-line industry indices are superior since other factors affect the optimal design of derivative contracts. For example, catastrophe contracts are likely to have reporting advantages and be less susceptible to manipulation by insurers than contracts based on industry loss ratios. The results nonetheless suggest that contracts based on industry loss ratios by state could be advantageous if these contracting problems could be resolved with other design features.
The third part of our analysis examines the effectiveness of hedging insurers' aggregate loss ratios across the twenty states for each line of business using two indices: (1) an industry aggregate catastrophe loss ratio for the twenty states in the sample, and (2) an insurer-specific catastrophe loss ratio, which is a weighted-average of the state catastrophe loss ratios where the weight is the insurer's earned premium in the state divided by the insurer's total earned premium across all 20 states in that line of business. The former index is analogous to hedging using a regional catastrophe contract, and the latter index is analogous to using an insurer-specific portfolio of state catastrophe contracts. For homeowners insurance, the median R-squared with the insurer-specific catastrophe loss ratio is almost double the median R-squared with the industry aggregate loss ratio. This finding suggests that derivatives based on state-specific catastrophe losses may allow insurers with different books of business across states to construct more effective hedges than by using a broader regional contract.
The next section of the paper describes the time series regression methodology used to estimate potential hedging effectiveness. We then discuss the major factors that affect interpretation of the results obtained using this methodology. This discussion includes results from our simulation analysis that sheds light on the implications of estimating hedging effectiveness with relatively small samples from a skewed distribution. The empirical results are then presented and discussed. The final section contains a short summary and our major conclusions.
HEDGING STATE-SPECIFIC LOSS RATIOS: METHODOLOGY
We use annual loss ratios for homeowners, fire, and commercial multiple peril insurance for individual insurer groups in twenty eastern and southern states during 1974 through 1994 to examine the potential effectiveness of hedging state-specific underwriting risk for these lines using (1) state-specific catastrophe loss indices and (2) state-specific industry loss indices for each line of business. (4) The twenty states are those included in the CBOT's Eastern PCS contract.
Each of the three lines of business cover property damage and therefore are subject to catastrophes to varying degrees. The major causes of catastrophe losses in our sample states during the period analyzed were weather-related events, such as windstorms and winter storms.(5) A priori, insurance derivatives would be expected to be most effective for homeowners insurance, given the vulnerability of residential properties to these sources of damage and the comparatively small liability loss component in homeowners losses. Hedging effectiveness might be similar for commercial multiple peril insurance sold to small to medium-sized businesses with modest liability exposures. However, commercial multiple peril coverage also includes coverage for large businesses with highly protected property (property that is highly resistant to damage) and, in many cases, a greater prevalence of large liability losses, so that overall hedging effectiveness could be lower than for homeowners. Hedging with catastrophe derivatives might be least effective for stand alone fire insurance, which commonly represents coverage for larger businesses with highly protected property, and in some cases may not include coverage for windstorm and other weather-related losses.
Estimating Potential Hedging Effectiveness
Basis risk declines and hedging effectiveness therefore increases as the correlation between unexpected changes in a firm's losses and unexpected changes in the underlying index increases. Ideally, we would like to estimate the correlation between the period t forecast error for an insurer's own loss ratio and the period t forecast error for the underlying index, where the forecasts are conditional on all information available at the beginning of period t. This section describes our procedure for estimating hedging effectiveness of contracts based on state-specific PCS catastrophe losses and compares the properties of the estimates to the ideal. Analogous procedures are used for measuring the hedging effectiveness of contracts based on state-specific industry losses.
The PCS catastrophe loss estimates are not allocated to specific lines of business. We therefore use the reported PCS loss estimates to create state-specific catastrophe indices by dividing the catastrophe loss estimate by the sum of premiums in the state for the three lines that we analyze.(6) For each state we provide evidence of R-squareds (-squared correlations) between firm loss ratios by line of business and this catastrophe loss ratio. The use of loss ratios in the analysis helps abstract from the effects of secular growth in expected catastrophe losses at the state level and in expected by-line, by-state claim costs at the firm level.
To simplify notation, we initially suppress state subscripts and line of business superscripts. Assume that insurer j's loss ratio (incurred losses/earned premiums) for a given line and state equals:
L[R.sub.jt] = [[Beta].sub.j]L[R.sub.Ct] + L[R.sub.Ojt], (1)
where L[R.sub.Ct] is the catastrophe loss ratio for the state (PCS catastrophe losses divided by industry earned premiums for the three lines in the state), L[R.sub.Ojt] is the insurer's loss ratio for non-catastrophe-related losses, and Cov(L[R.sub.Ct], L[R.sub.Ojt]) = 0. The parameter [[Beta].sub.j] reflects the sensitivity of the insurer's losses for the line and state to PCS catastrophe losses. This parameter will depend on the geographic distribution of the insurer's exposures in conjunction with the probability distribution of catastrophe losses by region. For example, an insurer with a relatively large (small) proportion of its insured properties in regions within a state that are most likely to experience catastrophe losses will have a relatively high (low) beta.
Without hedging, the insurer's underwriting risk for the line and state can be measured by the variance of its loss ratio, Var(L[R.sub.jt]) = [[[Beta.sub.j].sup.2]Var(L[R.sub.Ct]) + Var(L[R.sub.Ojt]). We follow the conventional procedure of measuring the potential hedging effectiveness in terms of the variance minimizing hedge, which is found by choosing the number of forward contracts to minimize the variance of the firm's loss ratio net of the payoff on the forward position (Ederington, 1979).(7) The payoff on a state-specific catastrophe forward contract (with $1 notional principle) equals L[R.sub.Ct] - FP, where FP is the known forward price when the contract is initiated. The number of contracts, [G.sub.j], per dollar of the firm's premiums in the line and state that minimizes Var[L[R.sub.jt] - [G.sub.j](L[R.sub.Ct] - FP)] equals [[Beta].sub.j]. The resulting underwriting risk is:
Var[L[R.sub.jt] - [[Beta].sub.j](L[R.sub.Ct] - FP)] = Var(L[R.sub.jt]) + [[[Beta].sub.j].sup.2] Var(L[R.sub.Ct]) - 2[[Beta].sub.j] Cov(L[R.sub.jt], L[R.sub.Ct]) (2)
From equation (1), Cov(L[R.sub.jt],L[R.sub.Ct]) = [[Beta].sub.j] Var(L[R.sub.Ct]); therefore, underwriting risk with the minimum variance hedge equals Var(L[R.sub.jt]) - [[[Beta].sub.j].sup.2] Var(L[R.sub.Ct]).
The percentage reduction in variance obtained through the variance minimizing hedge equals the coefficient of determination (R-squared) between the firm's loss ratio and the catastrophe loss ratio (Ederington, 1979):
[Mathematical Expression Omitted]. (3)
Intuitively, the R-squared measures the proportion of total underwriting risk that can be eliminated through the derivative contract. The R-squared is positively related to both [[Beta].sub.j] and the variance of L[R.sub.Ct], and it is negatively related to the variance of L[R.sub.Ojt] (recall that Var(L[R.sub.jt]) = [[[Beta].sub.j].sup.2]Var(L[R.sub.Ct]) + Var(L[R.sub.Ojt])). To obtain estimates of [R.sup.2](L[R.sub.jt], L[R.sub.Ct]), we estimate the following time series regression for each insurer j, for each line of insurance i, and each state s:
[Mathematical Expression Omitted] (4)
[Mathematical Expression Omitted] is a mean-zero disturbance. The R-squared from this model is used as an estimate of the population coefficient of determination. To measure the effectiveness of hedging using derivatives based on the state-specific industry loss ratio for each line of business, we also estimate the above equation substituting the industry loss ratio for the line and state (L[[R.sup.i].sub.It,s]) for the catastrophe loss ratio.
Insurers only report direct losses and premiums at the state level, as opposed to losses and premiums net of reinsurance. Reported PCS losses also are on a direct basis. Thus, our analysis relies exclusively on direct loss ratios. An advantage of estimating hedging effectiveness using direct loss ratios is that hedging with derivatives in principle can substitute for the purchase of reinsurance by primary insurers (or allow reinsurance prices to reflect the benefits of hedging the primary insurer's losses by the reinsurer).(8)
Behavior and Interpretation of Time Series R-squareds
The R-squared from a time series regression clearly may not provide an accurate measure of potential hedging effectiveness for a given insurer due to sampling error and possible instability over time in the population R-squared for an insurer. Broad evidence of the magnitude of historical correlations between insurer loss ratios and catastrophe (or industry) loss ratios should nonetheless provide significant insight into the general magnitude of basis risk and thus potential hedging effectiveness. Three issues arise with using the R-squared from a time series regression for this purpose: (1) the possible effects of extreme skewness in the underlying probability distribution of catastrophe losses on the sample R-squared estimated over a relatively short sample period, (2) the possible effects of predictable variation in realized loss ratios and in the ratio of expected catastrophe losses to total expected losses, and (3) the effect on R-squared of the insurer's own losses being included in the catastrophe (or industry) index.
Skewness in catastrophe loss distributions. A fundamental (and problematical) feature of catastrophe risk is the highly skewed distribution of catastrophe losses. Put simply, in any given year there is a comparatively high probability that catastrophe losses in a given state will be low or modest but a comparatively small probability of very large losses.(9) Our regressions use 21 years of data. In any given period of this length, it is likely that realized catastrophe losses will be low in some states simply due to chance. Conversely, in other states realized losses could be much greater than expected losses.
Intuitively, the R-squared for a twenty-one year sample period in states with no major catastrophe losses might be expected to understate the population R-squared as there will be little variation in the catastrophe loss ratio and thus little variation in the insurer's loss ratio attributable to catastrophe losses. Examination of the equation for the R-squared (equation (3)) reinforces this intuition. Low draws from the catastrophe loss distribution during the sample period will produce a low sample variance for the catastrophe loss ratio, which, for a given sample value of beta and of the variance of the non-catastrophe loss ratio, will cause the sample R-squared also to be low.(10) Thus, a low estimated R-squared for an insurer in a state that experienced low catastrophe losses during the sample period need not imply a low population R-squared. On the other hand, a low estimated R-squared for an insurer in a state that experienced large catastrophe losses would be informative as it would be unlikely to arise if the population R-squared were large.
Although the preceding intuition seems straightforward, the sample R-squared also depends on the sample covariance between a firm's loss ratio and the catastrophe loss ratio (and thus the sample beta). This covariance also could be affected by low or high draws from the catastrophe loss distribution. Thus, to provide additional insight and to check the validity of this intuition, we conducted a simulation analysis using the following assumptions:(11)
* The catastrophe loss ratio (L[R.sub.Ct]) can assume four discrete values: 0, 0. 1, 0.2, and 3.0, with probabilities of 0.6, 0.2, 0.15, and 0.05, respectively. Thus, there is a large probability that catastrophe losses will be zero or of modest size and a 5 percent probability of very large catastrophe losses. The expected value of the catastrophe loss ratio is 0.2 and the standard deviation is 0.649.
* The insurer's non-catastrophe loss ratio (L[R.sub.Ojt]) has the normal distribution with expected value of 0.6 and standard deviation of 0.15, and it is uncorrelated with L[R.sub.Ct]. These parameter values correspond roughly to the average sample mean and standard deviation of insurer loss ratios for homeowners insurance in states with small catastrophe losses during our sample period. (See tables 3 and 4 below.)
* The insurer's loss ratio is given by equation (1): L[R.sub.jt] = [[Beta].sub.j] L[R.sub.Ct] + L[R.sub.Ojt]. We consider two cases: [[Beta].sub.j] = 1 and [[Beta].sub.j] = 0.1 . When [[Beta].sub.j] equals 1, the population R-squared given by equation (3) equals 0.95 and the population variance of L[R.sub.j] equals 0.441. When [[Beta].sub.j] equals 0.1, the population R-squared equals 0.16, and the population variance of L[R.sub.j] equals 0.027.
[TABULAR DATA FOR TABLE 1 OMITTED]
We simulated 1,000 25-year samples of L[R.sub.Ct] and L[R.sub.Ojt] using these assumptions and then used equation (1) to calculate L[R.sub.jt] for [[Beta].sub.j] = 1 and [[Beta].sub.j] = 0. 1. We then calculated the sample R-squared and the sample beta (the sample covariance between L[R.sub.jt] and L[R.sub.Ct] divided by the sample variance of L[R.sub.Ct]) for each 25-year sample. Table 1 shows summary statistics for the simulated sample R-squareds, sample betas, and sample mean loss ratios over the 1,000 iterations.(12) Two key results are shown in the table. First, the simulated R-squared when the population beta equals 1.0 (and the population R-squared equals 0.95) is substantially less than the population R-squared for about twenty-five percent of the iterations, and the sample mean R-squared across the 1,000 iterations is 0.77, which is substantially less than the population R-squared. Second, the simulated R-squared when the population beta equals 0.1 has a sample mean across the 1,000 iterations of 0.18, which is approximately equal to the population R-squared of 0.16.
Figure 1 plots the simulated R-squareds against the 25-year mean catastrophe loss ratio for each iteration when [[Beta].sub.j] = 1. (Plots of R-squared against the standard deviation of the catastrophe loss ratio for the 25-year sample have similar implications.)(13) The message from Figure 1 is clear: low draws from the catastrophe loss distribution during a 25-year period, which produce a low sample variance and a low sample mean for the catastrophe loss ratio, frequently give rise to R-squareds that substantially understate the population R-squared. Large values of the sample mean (and sample variance) of the catastrophe loss ratio, however, produce R-squareds that are close to the population R-squared. In particular, whenever at least one large catastrophe loss ratio occurred in a 25-year period (producing a mean catastrophe loss ratio for the period of 0.12 or greater), the simulated R-squared is always close to or greater than 0.9 (compared to the population value of 0.95).
The simulation results therefore imply that finding low R-squareds in states that did not experience one or more large catastrophes during our sample period need not imply low population R-squareds. In addition, the estimated R-squareds for different states will be unable to distinguish states with low population R-squareds from those with high population R-squareds. However, the results imply that the time series R-squareds for states that experienced significant catastrophes should provide much better estimates of population R-squareds and potential hedging effectiveness. If basis risk is low, R-squareds should be high for states that experienced significant catastrophes. Conversely, low sample R-squareds in these states will likely reflect low population R-squareds, thus indicating substantial basis risk and low potential effectiveness of hedging with catastrophe derivatives.
While our empirical analysis focuses on R-squared as a measure of hedging effectiveness, the simulation results for beta are also of some interest. When the population beta equals 1.0, for example, the average estimated beta across the 1,000 iterations equals the expected value. Figure 2 plots the estimated betas against the 25-year mean catastrophe loss when [[Beta].sub.j] = 1. The beta estimates appear to be unbiased regardless of the level of catastrophe losses.(14) However, the beta estimates are tightly distributed around the expected value only when at least one large catastrophe occurs (producing a mean catastrophe loss ratio of at least 0.12). These results therefore suggest that significant catastrophe losses during a sample period are necessary for reliable estimation of beta. Variation in the estimated betas is very large when no large catastrophe loss ratio occurs during the sample period.
Predictable variation in loss ratios and expected claim costs. As suggested earlier, the appropriate measure of hedging effectiveness is the population R-squared between unexpected changes (forecast errors) in insurer loss ratios and unexpected changes in the catastrophe loss ratio. If the parameters in the simple model developed earlier do not vary over time, then the population R-squared between insurer loss ratios and the catastrophe loss ratio will equal the R-squared for unexpected changes in loss ratios. However, as shown in Appendix A, predictable variation in expected loss ratios or in the ratio of expected catastrophe losses to total expected losses (catastrophe and non-catastrophe) over time may cause the time series R-squared from a regression of an insurer's loss ratio on the catastrophe loss ratio to be a biased estimator of the R-squared for loss ratio forecast errors.
Harrington, Mann, and Niehaus (1995) estimated a loss ratio regression model that allowed for variation in expected loss ratios over time and used the model forecast errors as proxies for unexpected loss ratios (also see Hoyt and Williams 1995). We experimented with the same conditioning procedure, but large variability in state loss ratios at the company level and the highly skewed distribution of PCS catastrophe losses in some states led to unreliable parameter estimates and implausibly large variation in forecasts and unexpected loss ratios in some states. As a result, we focus on estimating unconditional R-squareds between L[R.sub.jt] and L[R.sub.Ct]. The possible bias due to predictable variation would appear to be of minor consequence compared to the possible effects of skewness in the catastrophe loss distribution that were discussed earlier.
Impact of firm's own losses on R-squared. Another potential concern arises because the catastrophe index includes an insurer's own catastrophe losses. Such inclusion will induce positive correlation between the index and an insurer's loss ratio. Since the correlation is expected to increase with the insurer's market share, the concern is that a high R-squared could arise simply because the particular insurer had a high market share. However, as the analysis shown in Appendix A indicates, the correlation due to an insurer's own losses being included in the index is unlikely to inflate the R-squared significantly for most insurers.
HEDGING STATE-SPECIFIC LOSS RATIOS: EMPIRICAL RESULTS
Catastrophe loss ratios. The catastrophe loss ratio is defined as PCS's catastrophe loss estimate in state s during year t divided by the sum of homeowners multiple peril, commercial multiple peril, and fire insurance earned premiums in state s during year t. Table 2 provides means, medians, and standard deviations for the catastrophe loss ratio for each state. The mean exceeds the median in every state, indicating skewness in catastrophe loss ratios. The southern states of Alabama, Florida, Louisiana, Mississippi, North Carolina, and South Carolina have the highest means and standard deviations, reflecting the relatively high hurricane losses during the sample period for these states. The standard deviations for South Carolina and Florida are much larger than for the remaining states due to Hurricane Hugo in 1989 and Hurricane Andrew in 1992. Note, however, that the median catastrophe loss ratios are four to five times larger in Alabama and Mississippi than in Florida and South Carolina, indicating more frequent but less severe catastrophe losses than in the latter two states.
TABLE 2 Catastrophe loss ratios defined as PCS's estimate of the state's annual catastrophe losses divided by the state's earned premiums in homeowners, commercial multiple peril, and fire insurance over the 1974 through 1994 period. Catastrophe loss ratios State Mean Median Std. Dev. AL 20.7 9.2 39.6 CT 4.2 2.8 4.5 DE 7.6 4.8 10.3 FL 39.7 2.2 156.6 GA 8.6 6.0 9.9 LA 11.4 4.1 18.8 MA 3.8 1.4 5.9 MD 2.8 2.3 2.4 ME 2.7 0.9 4.4 MS 20.2 10.1 33.6 NC 10.0 2.0 21.9 NH 1.7 0.0 3.6 NJ 2.6 1.7 3.0 NY 2.7 1.0 4.0 PA 3.3 1.4 4.1 RI 8.1 2.8 13.1 SC 29.6 2.3 110.1 VA 3.9 1.6 6.4 VT 2.5 0.0 5.5 WV 7.0 4.4 10.7 Average 9.2 2.9 22.3
Line-specific industry loss ratios. The statewide industry loss ratios for homeowners, commercial multiple peril, and fire insurance equal direct incurred losses divided by direct premiums earned for the line and state as reported by Best's Executive Data Service. The means, medians, and standard deviations for the state loss ratios are reported in Table 3. In both the homeowners and commercial multiple peril lines, the southern states again tend to have the highest mean industry loss ratios and the highest standard deviations. With the exception of Florida and to a smaller extent Louisiana, the standard deviations of fire insurance loss ratios in the southern states generally are not greater and sometimes are much smaller than the standard deviations in other states (see, e.g., the fire insurance standard deviations for Alabama, Mississippi, North Carolina, and South Carolina). This result is consistent with a limited effect of catastrophe losses on volatility in statewide fire insurance loss ratios in these catastrophe-prone states, perhaps due to a greater prevalence of coverage for highly protected commercial property in the fire insurance line.
[TABULAR DATA FOR TABLE 3 OMITTED]
Descriptive statistics for insurer loss ratios. Annual loss ratios (direct incurred losses to direct premiums earned) for insurer groups in the twenty states were obtained from Best's Executive Data Service over the period 1974 through 1994 for the three lines of business. For each line and each state, we eliminate groups that have fewer than fifteen years of data and whose minimum earned premiums over the entire sample period are less than $100,000. These restrictions leave thirty-eight groups that write in at least one of the three lines of business in at least one of the twenty states. The groups are listed in Appendix B along with the number of states in which they meet the sample selection criteria in each of line of business. Summing across insurers, states, and lines of business, there are 1,141 separate time series of loss ratios.
Table 4 reports the mean and median values across insurers of the time series standard deviations of the insurers' loss ratios. On average, homeowners insurance appears to have the lowest volatility in loss ratios, despite extremely high volatility in a few states (Florida and South Carolina).(15) For both homeowners and commercial multiple peril insurance, the states with the highest median values for the loss ratio standard deviations are again in the south.
[TABULAR DATA FOR TABLE 4 OMITTED]
[TABULAR DATA FOR TABLE 5 OMITTED]
[TABULAR DATA FOR TABLE 6 OMITTED]
Estimates of Hedging Effectiveness
State-specific PCS catastrophe derivatives. Table 5 summarizes the R-squareds from estimating equation (4). Comparing the R-squareds across lines of business, the results indicate that, on average, the catastrophe loss ratio is most effective in hedging homeowners underwriting risk and least effective in hedging the fire line.(16) Within both homeowners and commercial multiple peril, considerable variation in R-squareds exists across states.
As predicted by the discussion and simulation analysis of the effects of skewness in catastrophe loss distributions, the southern states that experienced significant catastrophe losses generally have higher mean and median values of R-squared and a relatively high proportion of insurers with statistically significant R-squareds.(17) Figure 3 illustrates this relationship for homeowners insurance by plotting the median R-squared against the mean catastrophe loss ratio by state. The overall results in Table 5 suggest that state-specific catastrophe loss ratios are strongly related to insurers' state and line specific loss ratios for the states and lines of business that experienced significant catastrophe losses during the 1974 through 1994 period.(18)
State-specific industry loss ratios. Table 6 summarizes the estimates of hedging effectiveness using the by-line, by-state industry loss ratios. A comparison of Table 6 with Table 5 indicates that use of the industry loss ratio results in higher R-squareds on average for each line of business. This result, which is illustrated graphically in Figure 4 for the homeowners line, suggests that there are material sources of correlation in state loss ratios that are not captured fully by catastrophe loss ratios. The largest increase in R-squared is for the states that have relatively low catastrophe losses during the sample period, i.e., the northern states. In contrast, in the southern states, the R-squareds using the industry loss ratios generally increase little compared to the R-squareds using the catastrophe loss ratios. This result is not surprising given the greater overall volatility in by-line loss ratios in the southern states that results from greater volatility in catastrophe losses.(19)
To examine the statistical significance of the additional explanatory power of the industry loss ratio relative to the catastrophe loss ratio, we estimate time series regressions for insurer loss ratios where both the catastrophe loss ratio and the industry loss ratio are explanatory variables. A test of the statistical significance of the coefficient for the industry loss ratio in this multivariate regression model indicates whether an insurer could significantly reduce underwriting risk using both a catastrophe loss ratio contract and an industry loss ratio contract compared to using a catastrophe loss contract alone. The results are presented in the final column of each panel of Table 6. In each of the lines of business, the coefficient on the industry loss ratio is statistically significant for a relatively high proportion of insurers in many states. The mean proportion of homeowners insurers across the twenty states with a statistically significant coefficient is 54.2 percent. Thus, for a large number of homeowners insurers hedging using both a catastrophe loss ratio contract and an industry loss ratio contract would significantly reduce underwriting risk relative to just using a catastrophe loss ratio contract. This evidence suggests that homeowners insurers are subject to material correlation in state loss ratios beyond the correlation due to catastrophes.(20)
Hedging Multi-State Loss Ratios
An important issue that confronts designers of derivative contracts is whether to construct multi-state regional contracts or state-specific contracts.(21) One factor affecting this choice is whether a menu of state specific contracts would allow insurers to reduce basis risk significantly compared to a single regional contract. To address this issue, we compare R-squareds obtained by regressing an insurer's aggregate loss ratio across the twenty states for each line of business on two alternative catastrophe loss ratio indices. The first index is the aggregate catastrophe loss ratio for the twenty state sample, which equals the weighted average of the state-specific catastrophe loss ratios, where the weight for each state equals industry premiums for the three lines in the state divided by total industry premiums for the three lines in the twenty state sample. The second index is an insurer-specific catastrophe loss ratio, which equals the weighted average of the state-specific catastrophe loss ratios, where the weight for each state equals the company's premiums in the line and state divided by the company's total premiums in the line for the twenty state sample. Comparison of the R-squareds for these two approaches provides evidence of the potential improvement in hedging an insurer's multi-state loss experience that might be achieved from using an insurer specific portfolio of state contracts compared to using a regional contract. The insurer-specific portfolio approach is likely to reduce basis risk that would arise from variation in the insurer's market shares across states.
Table 7 summarizes the R-squareds from regressing the aggregate by-line insurer loss ratios for the twenty states on the industry aggregate multi-state catastrophe loss ratio and the insurer-specific multi-state loss ratios. The results indicate that the insurer-specific catastrophe loss ratio produces higher R-squareds and a higher proportion of statistically significant R-squareds in the homeowners line, but not the other lines of business. For example, the median R-squared using the insurer-specific catastrophe loss ratio for the twenty states is 63.8 percent compared to 33.5 percent using the industry aggregate catastrophe loss ratio. We conducted a J-test (see Kmenta 1986; and footnote 20) to provide evidence of whether the greater explanatory power for the insurer-specific catastrophe loss ratio than for the industry aggregate catastrophe loss ratio for many homeowners insurers is statistically significant. Using the J-test, the model with the industry aggregate catastrophe loss ratio is rejected in favor of the model with the insurer-specific catastrophe loss ratio in sixty-five percent of the cases. The model with the insurer-specific catastrophe loss ratio is rejected in favor of the model with the industry aggregate catastrophe loss ratio in only three percent of the cases.(22)
TABLE 7 Summary statistics for R-squareds from regressions of insurer by line loss ratios across all twenty states on the 20-state industry aggregate catastrophe loss ratio and (b) the insurer-specific 20-state catastrophe loss ratio Homeowners Cat. Loss Ratio Mean Median % Sign. Industry Aggregate 45.0 33.5 59.5 Insurer-Specific 57.6 63.8 83.8 Commercial Multiple Peril Cat. Loss Ratio Mean Median % Sign. Industry Aggregate 19.3 10.1 41.9 Insurer-Specific 21.3 17.2 48.4 Fire Cat. Loss Ratio Mean Median % Sign. Industry Aggregate 10.0 2.5 8.6 Insurer-Specific 9.3 3.1 8.6 Note: The 20-state industry aggregate catastrophe loss ratio is the weighted-average of state specific catastrophe loss ratios using the industry's earned premium in the state across all three lines divided by industry earned premium in all three lines across all twenty states as weights. The insurer-specific catastrophe loss ratio is the weighted-average of state specific catastrophe loss ratios using the insurer's earned premium in the state for that line divided by the insurer's earned premium in that line across all twenty states as weights. The sample sizes are 37, 31, and 35 insurers for homeowners, commercial multiple peril, and fire insurance, respectively.
These results suggest that derivatives based on state-specific catastrophe losses would allow insurers with different books of business across states to construct significantly better hedges than with a broader regional contract. They are thus analogous to the results obtained by Major (1996) using simulated catastrophe losses at the state and zip code level.
We also estimated a multivariate regression model where both the industry aggregate multi-state catastrophe loss ratio and the insurer-specific multi-state catastrophe loss ratio are explanatory variables. The statistical significance of the coefficient on the insurer-specific multi-state catastrophe loss ratio provides a test of whether using an insurer specific weighted portfolio of state catastrophe contracts along with a regional contract allows a significant reduction in underwriting risk relative to using the regional contract alone. The results indicate that the coefficient on the insurer-specific portfolio is statistically significant for 33.8 percent, 3.2 percent, and 1.4 percent of the insurers in the homeowners, commercial multiple peril, and fire lines, respectively. Along with the results in Table 7, this evidence suggests that many homeowners insurers would be able to reduce risk materially by augmenting a position in a regional contract with a portfolio of positions in state contracts.
SUMMARY AND CONCLUSION
Our study provides evidence on the potential effectiveness of state-specific insurance derivatives in hedging underwriting risk by relating individual insurer groups' annual loss ratios for homeowners, commercial multiple peril, and fire insurance in twenty eastern and southern states during the 1974 through 1994 period to state-specific catastrophe loss ratios using PCS catastrophe loss data. We also relate the insurer loss ratios to industry statewide loss ratios for each line of business. Finally, we examine the effectiveness of hedging insurers' aggregate loss ratios for the twenty states for each line using two indices: (1) an industry aggregate catastrophe loss ratio for the twenty states in the sample, and (2) a insurer-specific 20-state catastrophe loss ratio equal to the weighted-average of the state-specific catastrophe loss ratios with weights equal to each state's share of the insurer's total earned premiums for the twenty sample states in the given line of business.
Our three main results can be summarized as follows: First, the R-squareds from the regressions of insurer by line and state loss ratios on state catastrophe loss ratios suggest that state-specific PCS catastrophe derivatives would be effective hedges against variation in insurer by line and state loss ratios. This result implies that state-specific catastrophe derivatives could be used effectively to hedge many insurers' aggregate underwriting results across lines and states. In those states that experienced significant catastrophe losses during the sample period, insurers' loss ratios are generally highly correlated with state catastrophe loss ratios, especially for the homeowners line. The R-squareds from the regressions of insurer by-line and state loss ratios on state catastrophe loss ratios generally are low for states with comparatively low catastrophe losses during the sample period. However, intuition and the results of our simulation of empirical R-squareds suggest that this latter result is likely even if the population R-squared between insurer and catastrophe loss ratios is high.
Second, our results suggest that industry by line and state loss ratios, on average, could provide more effective hedges than by line and state catastrophe loss ratios, as indicated by the generally higher mean and median R-squareds using the industry loss ratios. We have not, however, explored whether loss reporting or other contract design issues might nullify the potential benefits of contracts based on industry loss ratios.
Third, our results on average indicate materially better hedging of multi-state insurer loss ratio risk for homeowners insurance using state-specific contracts rather than regional contracts. This result suggests that derivatives on state-specific catastrophe losses may allow homeowners insurers with different books of business in different states to construct materially more effective hedges than with a broader regional contract.
The Effect of Predictable Variation in Expected Loss Ratios and Expected Claim Costs
To illustrate the potential bias resulting from predictable variation in expected loss ratios or in the ratio of expected catastrophe losses to total expected losses, let the firm's unexpected loss ratio for period t in a given line and state (i.e., its loss ratio forecast error for period t) equal UL[R.sub.jt] [equivalent to] L[R.sub.jt] - E(L[R.sub.jt]) and the unexpected catastrophe loss ratio equal UL[R.sub.Ct] [equivalent to] L[R.sub.Ct] - E(L[R.sub.Ct]), where E() denotes expected value conditional on information available at the beginning of period t; and L[R.sub.Ct] = [L.sub.Ct]/[P.sub.It], where [P.sub.It] equals industry premiums for catastrophe-related lines in the state. Also, assume that insurer j's premiums for the line and state in period t equal [P.sub.jt] = [[[Lambda].sub.jt].sup.-1]E([L.sub.jt]) and that industry premiums for catastrophe-related lines in the state equal [P.sub.It] = [[[Lambda].sub.It].sup.-1]E([L.sub.It]), where [L.sub.jt] and [L.sub.It] equal losses incurred for insurer j and for the industry, respectively; and [[[Lambda].sub.jt].sup.-1] and [[[Lambda].sub.It].sup.-1] are premium loading factors for transaction costs, capital costs, and discounting of expected claim costs to present value for insurer j and for the industry, respectively. Given these definitions, [[Lambda].sub.jt] is firm j's expected loss ratio for the line and state, and [[Lambda].sub.It] is the industry expected loss ratio for catastrophe-related lines in the state. Substituting these expressions into the definitions of L[R.sub.jt] and L[R.sub.Ct], [R.sup.2](L[R.sub.jt], L[R.sub.Ct]) is given by:
[Mathematical Expression Omitted], (A1)
where [a.sub.t] [equivalent to] E([L.sub.Ct])/E([L.sub.It]) is the ratio of expected catastrophe losses to total expected losses for catastrophe-related lines for the industry in the state in period t.
Conditional on [[Lambda].sub.jt], [[Lambda].sub.It], and [a.sub.t] (and assuming stability in the R-squared between UL[R.sub.jt] and UL[R.sub.Ct]), or, equivalently, if these variables are time invariant, expression (A1) reduces to [R.sup.2](UL[R.sub.jt], UL[R.sub.Ct]). Thus, under these assumptions (and apart from the effects of skewness in catastrophe loss ratio distributions), the R-squared from a time series regression of the insurer's loss ratio on the catastrophe loss ratio would provide an unbiased estimate of the R-squared for unexpected changes in loss ratios. However, given possible variation over time in expected loss ratios ([[Lambda].sub.jt] and [[Lambda].sub.It]) and any possible trend in the ratio of expected catastrophe losses to expected total industry losses for catastrophe-related lines ([a.sub.t]), the R-squared from a time series regression also will reflect correlations over time between [[Lambda].sub.jt], [[Lambda].sub.It], and [a.sub.t], thus provide a biased measure of hedging effectiveness.
In particular, some of the variability in loss ratios will reflect variation in expected loss ratios due to changes in interest rates and declining expense loadings over time. In addition, evidence of time series variation in loss ratios suggests that expected loss ratios could follow a second order autoregressive process (e.g., Cummins and Outreville 1987). Variation in expected loss ratios and thus premiums rates from these and other sources might cause [R.sup.2](L[R.sub.jt], L[R.sub.Ct]) to be an upward biased measure of the squared correlation between the true conditional forecast errors.
Effect of a Firm's Own Losses on R-squared
To illustrate the effect of a firm's own losses on R-squared, we assume that there is no correlation between an insurer's loss ratio and the catastrophe loss ratio apart from the correlation due to the inclusion of the insurer's own catastrophe losses in the catastrophe loss ratio index. The R-squared between insurer j's loss ratio in line i and the catastrophe loss ratio for a particular state is defined as:
[Mathematical Expression Omitted], (B1)
where [Mathematical Expression Omitted] is firm j's loss ratio in line i and L[R.sub.C] is the state catastrophe loss ratio. Letting [Mathematical Expression Omitted] equal insurer j's premiums in line i and [Mathematical Expression Omitted] equal firm j's catastrophe loss ratio in line i, the state-wide catastrophe loss ratio can be written as a weighted average of individual insurers' catastrophe loss ratios:
[Mathematical Expression Omitted] (B2)
where [[m.sub.j].sup.i] equals [Mathematical Expression Omitted], which is insurer j's market share in line i relative to all catastrophe related lines in the state.
Under the premise that insurer j's loss ratio in line i is uncorrelated with catastrophe losses of other insurers [Mathematical Expression Omitted] for all i,h and all j[not equal to]k) and uncorrelated with its own catastrophe losses in other lines,(23) the numerator of expression B1 equals [Mathematical Expression Omitted]. Assuming insurer j's overall loss ratio in line i is perfectly positively correlated with its catastrophe loss ratio in line i, the numerator simplifies to the following expression:
[Mathematical Expression Omitted] (B3)
Regarding the denominator of expression B1, if we assume a constant variance of catastrophe loss ratios across insurers and lines of business (Var(L[[R.sup.i].sub.Cj]) = Var(L[[R.sup.h].sub.Ck]) for all i,h, and all j,k) and zero covariance between the catastrophe loss ratios across insurers and lines of business (which will understate the denominator and thus inflate the R-squared), the denominator equals
[Mathematical Expression Omitted] (B4)
Taking the ratio of B2 and B3, the R-squared equals
[Mathematical Expression Omitted].
For our sample, the median value of [Mathematical Expression Omitted] across all lines and states is 0.0006, the 75tn percentile value is 0.006, and the 95tn percentile value is 0.09. As a result, the value of R-squared that would arise simply from the inclusion of a firm's own losses in the catastrophe loss ratio is negligible for most firms.
Sample insurers and number of eastern and southern states in which each insurer group has at least fifteen years of loss ratio data and a minimum value of earned premiums greater than $100,000 during 1972 through 1994 (maximum number of states is twenty).
Commercial Multiple Insurer Group Homeowners Peril Fire Aetna Life & Casualty 18 19 20 Allstate Insurance 18 1 13 American Financial 13 16 14 American General 0 0 7 American International Group 1 10 16 Lincoln National 3 2 2 Amica Mutual 9 0 0 Atlantic Mutual 5 10 2 Chubb 13 17 13 CNA Insurance 12 19 13 Colonial Penn 4 0 0 Commercial Union 17 19 11 Continental 17 18 15 Crum & Forster 10 14 13 Fireman's Fund 12 19 16 GEICO 11 0 2 General Accident 13 9 13 Hanover Insurance 14 9 10 ITT Hartford 19 20 19 Home Insurance 3 15 15 Kemper National 12 18 4 Liberty Mutual 16 14 11 Nationwide 16 12 12 Ohio Casualty 8 4 7 Prudential of America 10 0 0 Reliance Insurance 6 13 10 Royal Insurance 10 15 17 Safeco Insurance 10 8 6 Sentry Insurance 5 0 7 St. Paul 9 13 19 State Farm 19 13 3 Transamerica Insurance 6 5 4 Travelers Insurance 17 18 14 United States F&G 16 18 16 Unigard Insurance 2 2 1 USAA 11 0 0 Utica National 10 10 3 Zurich Insurance 4 9 7 Total 399 389 355
1 Several other studies have used this approach. D'Arcy and France (1992) examine the basis risk associated with national catastrophe contracts for nine insurers. Using a larger sample of insurers, Harrington, Mann, and Niehaus (1995) examine basis risk associated with both the national catastrophe contracts and national contracts based on individual lines of insurance. Hoyt and Williams (1995) estimated basis risk by line of insurance using nationwide experience for the nine insurers analyzed by D'Arcy and France (1992). Litzenberger, et al. (1996) also utilizes historical data to price catastrophe derivative contracts.
2 The Florida insurance commissioner has expressed concern about the use of computer simulation models for rate setting purposes because of the uncertainty about the reliability of the underlying assumptions.
3 As explained below, we also provide evidence related to the latter issue. Thus, by analyzing by line and state loss ratios for individual insurers, we are not suggesting that insurer hedging strategies should focus on by line and state results, as opposed to aggregate results across multiple lines and states.
4 To reduce the cost of hand collecting twenty-one years of insurer group data for each of the twenty states, we limited the analysis to three lines of business that provide property damage coverage. As discussed below, these lines are subject to catastrophes to different degrees and therefore they provide some cross-sectional variation in the extent to which catastrophe contracts would likely be effective hedges. Existing research also suggests that hedging effectiveness for national contracts varies by line of business (Harrington, Mann, and Niehaus 1995; Hoyt and Williams 1995).
5 Since the PCS contracts traded at the CBOT are based on catastrophe losses due to a number of perils, we do not construct catastrophe loss ratios based on specific perils. Data limitations also would hinder such an effort for while the PCS data list the perils that caused the losses for a given catastrophe (e.g., wind, ice, and snow), only an aggregate loss amount for the catastrophe is reported. Eighty-nine percent of the 1,125 catastrophes in our sample list windstorms or tornadoes as a peril. Only 0.7 percent of the catastrophes list fires or explosions as a peril.
6 We also experimented with using catastrophe loss ratios defined as PCS catastrophe losses divided by industry premiums for the given line in the state and obtained qualitatively similar results concerning hedging effectiveness.
7 Note that we are not suggesting that minimizing the variance of this loss ratio (or an aggregate loss ratio across multiple lines and states) is the appropriate goal of the firm. Firm value maximization can motivate firms to hedge in order to reduce expected costs of financial distress and bond promises to pay claims to policyholders that are concerned with and informed about insolvency risk. The value maximizing strategy will depend on these benefits and the availability and costs of hedging instruments, as well as the costs of reinsurance and equity and debt capital.
8 A number of coastal states have beach and windstorm plans that provide windstorm coverage on properties located within specified coastal regions if property owners are unable to obtain coverage in the standard market. These plans require all insurers that write property insurance in the state to share in the financial results of the plan. By-state direct losses and premiums reported by insurers generally do not reflect the results of the plan. The estimated R-squareds in this study will tend to underestimate potential effectiveness of hedging loss ratios that reflect beach and windstorm plan experience with derivatives based on indices that include this experience.
9 State catastrophe loss and loss ratio distributions will likely exhibit greater skewness than the distributions of nationwide catastrophe losses and loss ratios.
10 We note, somewhat trivially, that in the limiting case of zero catastrophe losses, the catastrophe loss ratio would be zero each year and the sample covariance between an insurer's loss ratio and the catastrophe loss ratio would be zero even if the population R-squared were large.
11 These simple assumptions should be sufficient to provide evidence of the likely behavior of R-squared as a function of realized catastrophe loss ratios. A more elaborate approach would estimate the probability distribution for the catastrophe loss ratio and use the estimated distribution in the simulation. Cummins, Lewis, and Phillips (1996) use PCS loss data to estimate the probability distribution of catastrophe losses in order to price potential government catastrophe reinsurance arrangements.
12 The sample mean and standard deviation of the simulated 25-year mean loss ratios correspond closely to the population values. The standard deviation of the 25-year mean equals the standard deviation of the annual loss ratio divided by 5, which is the square root of the sample size.
13 The sample means and standard deviations for the catastrophe loss ratios are highly correlated (correlation coefficient equals 0.93 when [[Beta].sub.j] = 1), which is expected given the skewness of the catastrophe loss ratio distribution.
14 The correlation coefficient between the beta estimates and the mean catastrophe loss ratio equals -0.04 when [[Beta].sub.j] = 1.
15 The lower volatility for homeowners might be due to the greater exposure base and smaller average exposure size for this coverage than for the commercial coverages. Homeowners also might be influenced by possibly lower variation in expected loss ratios than in commercial lines (e.g., due to less cyclical variation in premium rates or greater variation in commercial lines premiums in the early and mid 1980s associated with the liability insurance crisis).
16 For national contracts, Harrington, Mann, and Niehaus (1995) and Hoyt and Williams (1995) find that catastrophe derivatives are also most effective in hedging homeowners underwriting risk.
17 Consistent with the simulation analysis, the R-squareds for the southern states decline substantially if 1989 and 1992 (the years of hurricane Hugo and Andrew) are omitted from the analysis.
18 By line regressions of the sample R-squareds on the natural logarithm of an insurer's mean earned premiums in the line and state (or alternatively on its mean market share) and state dummy variables indicated a positive and significant relationship between R-squared and the logarithm of mean earned premiums. While inclusion of an insurer's own losses in the index could contribute to this result, Appendix A suggests that this effect is small. The relation could be due to insurers with lower premium volume on average having a higher variance of the non-catastrophe loss ratio and thus higher basis risk and a lower R-squared.
19 In contrast to the catastrophe index, we are able to exclude an insurer's own losses from the industry by line and state loss ratio index. The results presented for the industry index (Table 6) are not materially altered by the exclusion of each insurer's results from the index.
20 To test whether using an industry loss ratio contract alone would be more effective than using a catastrophe loss ratio alone, we used a J-test, which tests one model against the other and vice versa (Kmenta 1986). The catastrophe loss ratio model is rejected against the industry loss ratio model in 56 percent of the cases for homeowners, thirty-eight percent of the cases for commercial multiple peril, and 24 percent of the cases for fire. In contrast, the industry loss ratio model is rejected in favor of the catastrophe loss ratio model in ten percent of the cases for homeowners, thirteen percent of the cases for commercial multiple peril, and twelve percent of the cases for fire. Similar results are found using the JA-test (Kmenta 1986).
21 The CBOT currently offers both types, but only three state-specific contracts. The disaggregation issue can be carried further by asking whether county indices are better than state indices and whether zip code indices are better than county indices. As noted, disaggregation may reduce the liquidity of any individual contract.
22 Not surprisingly given the results in Table 7, the J-test results provided little evidence in favor of either model for commercial multiple peril coverage and fire insurance. For commercial multiple peril, the industry aggregate catastrophe loss ratio model was rejected in favor of the insurer-specific catastrophe loss ratio model in seven percent of the cases; the insurer-specific catastrophe loss ratio model was rejected in favor of the industry aggregate catastrophe loss ratio model in none of the cases. The corresponding percentages for fire insurance were both three percent.
23 Allowing correlation across lines for a particular insurer would not affect the conclusion of this analysis.
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Scott Harrington is a Professor of Insurance and Finance and a Francis M. Hipp Distinguished Faculty Fellow at the University of South Carolina. Greg Niehaus is an Associate Professor of Insurance and Finance at the University of South Carolina. The authors appreciate the research assistance of Congsheng Wu, Soegku Byoun, and Karen Epermanis, the generosity of Property Claims Service for providing the catastrophe loss data, and the comments of Richard MacMinn, an anonymous reviewer, Steven Mann, Craig Merrill, and participants at the 5th International Insurance Solvency Conference in London 1997 and the 1997 American Risk and Insurance Association Meetings.
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|Title Annotation:||Property Claims Service|
|Author:||Harrington, Scott; Niehaus, Greg|
|Publication:||Journal of Risk and Insurance|
|Date:||Mar 1, 1999|
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