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Long-range dependence in daily volatility on Tunisian stock market.

ABSTRACT

The aim of this paper is to surround the volatility dynamics on the Tunisian stock market via an approach founded on the detection of persistence phenomenon and long-term memory presence. More specifically, our object is to test whether long-term dependent processes are appropriated for modelling Tunisian stock market volatility. The empirical investigation has been driven on the two Tunisian stock market indexes IBVMT and TUNINDEX for the period (1998-2004) in daily frequency. Through the estimation of FIGARCH processes, we show that long-term component of volatility has an impact on stock market return series.

JEL: C22, C52

Keywords: Volatility; Long-term memory; Fractional integration; FIGARCH process

I. INTRODUCTION

Volatility persistence is a subject that has been thoroughly investigated since the introduction of ARCH models by Engle (1982). It is not only important in forecasting future market movements but also is central to a host of financial issues such as portfolio diversification, risk management, derivative pricing and market efficiency. Although, it is common to find a significant statistical relationship between current measures of volatility and lagged values, it has been very difficult to find models that adequately specify the time series dependencies in volatilities in speculative returns data. Ding, Granger and Engle (1993) show that stock market absolute returns exhibit a long-memory property in which the sample autocorrelation Autocorrelation

The correlation of a variable with itself over successive time intervals. Sometimes called serial correlation.
 function decay very slowly and remain significant even at high order lags. Evidence in favour of long-range dependence in measure of volatility has been largely documented. Despite the fact that emerging markets in the last two decades had attracted the attention of international investors as means of higher returns such as with diversification of international portfolio risk. Few studies had investigated the issue of volatility persistence using nonlinear A system in which the output is not a uniform relationship to the input.

nonlinear - (Scientific computation) A property of a system whose output is not proportional to its input.
 estimation models.

Emerging markets differ from developed markets. The former are in most, cases are characterized the by lack of institutional development, thinly traded Thinly traded

Infrequently traded.
 markets, lack of corporate governance Corporate Governance

The relationship between all the stakeholders in a company. This includes the shareholders, directors, and management of a company, as defined by the corporate charter, bylaws, formal policy, and rule of law.
 and market microstructure Market microstructure

The functional setup of a market.
 distortions. Theses factors hinder the flow of information to market participants. Moreover, in most of these markets, participants slowly react to information due to the lack of equity culture. This paper will focus on Tunisian Stock Exchange (henceforth From this time forward.

The term henceforth, when used in a legal document, statute, or other legal instrument, indicates that something will commence from the present time to the future, to the exclusion of the past.
, TSE See Tokyo Stock Exchange.

TSE

1. See Tokyo Stock Exchange (TSE).

2. See Toronto Stock Exchange (TSE).
) revisiting the issue of volatility persistence in stock market returns. We attempt to investigate empirically market returns, volatility persistence in a distinct approach from previous researches and this by testing for presence of fractional dynamics (i.e. long memory process in TSE volatility). Thus, this investigation proves to be a first essay in the Tunisian context. As we raised, the categorical That which is unqualified or unconditional.

A categorical imperative is a rule, command, or moral obligation that is absolutely and universally binding.

Categorical is also used to describe programs limited to or designed for certain classes of people.
 absence of empirical studies Empirical studies in social sciences are when the research ends are based on evidence and not just theory. This is done to comply with the scientific method that asserts the objective discovery of knowledge based on verifiable facts of evidence.  founded on the fractional integrated behaviour in 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 Tunisian stock returns. Data used are the two Tunisian stock indexes (IBVMT index and TUNINDEX) daily returns during the period from December 31, 1997 till April 16, 2004. The empirical results provided evidence that the daily stock market volatility exhibits long-range dependency. The fractional integrated behaviour in the conditional variance of the daily Tunisian stock indexes have important implications on efficiency tests and on optimal portfolio allocations and consequently for optimal hedging decisions. The remaining sections are organized as follows. The next respected on the theoretical background of long memory and discusses its measurement. Section III presents some practical considerations of long memory processes. Section IV provides an overview on the Tunisian stock market while section V reviews the fractionally integrated GARCH GARCH Generalized Autoregressive Conditional Heteroskedasticity  model. Results are presented in section VI with conclusions in section VII.

II. THEORETICAL BACKGROUND

To this level, it seems to be worth to elucidate e·lu·ci·date  
v. e·lu·ci·dat·ed, e·lu·ci·dat·ing, e·lu·ci·dates

v.tr.
To make clear or plain, especially by explanation; clarify.

v.intr.
To give an explanation that serves to clarify.
 the conceptual issues of volatility, standard deviation and risk. In financial market theory, volatility is often used to refer to standard deviation, [sigma] or variance [[sigma].sup.2], estimated from an historical return time as follows:

[[sigma].sup.2] = 1/N - 1 [N.summation summation n. the final argument of an attorney at the close of a trial in which he/she attempts to convince the judge and/or jury of the virtues of the client's case. (See: closing argument)  over (t=1)] [([r.sub.t] - [bar.r]).sup.2]

where [bar.r] is the mean return. The sample standard deviation statistic [sigma] is the distribution free parameter The introduction to this article provides insufficient context for those unfamiliar with the subject matter.
Please help [ improve the introduction] to meet Wikipedia's layout standards. You can discuss the issue on the talk page.
 representing the second moment characteristic of the sample. Only when [sigma] is attached to a standard distribution, such as normal or Student-t distributions, the required probability density probability density
n. Statistics In both senses also called probability distribution.
1. A function whose integral over a given interval gives the probability that the values of a random variable will fall within the interval.
 and cumulative probability density can be derived analytically. In fact, [sigma] can be estimated from an irregular shape distribution, in which case the probability density will have to be derived empirically. In a continuous time context, [sigma] is a scale parameter In probability theory and statistics, a scale parameter is a special kind of numerical parameter of a parametric family of probability distributions. Definition
If a family of probability densities with parameter s is of the form

 that multiplies and reduces the size of the fluctuations generated by a standard Wiener process In mathematics, the Wiener process is a continuous-time stochastic process named in honor of Norbert Wiener. It is often called Brownian motion, after Robert Brown. It is one of the best known Lévy processes (càdlàg stochastic processes with stationary independent . Indeed, different shapes of financial assets Financial assets

Claims on real assets.
 returns are specified either through the dynamic of the underlying 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.
 or whether or not the parameter are time varying. Therefore, it would be disjointed to assimilate as·sim·i·late
v.
1. To consume and incorporate nutrients into the body after digestion.

2. To transform food into living tissue by the process of anabolism.
 the standard deviation to a good measure of risk dice at the time of that it is neither attached to a distribution data to a dynamics of assessment. In the same way, the using of the standard deviation as measure of uncertainty often implicitly implies the presence of a normal distribution in the financial assets returns distribution. However, the junction between concepts of volatility and risk is ambiguous. In particular, the risk is often associated to a possible presence of weak or negative returns; whereas, most measures of distribution make no such distinction (e.g., Poon poon  
n.
Any of several trees of the genus Calophyllum, of southern Asia, having light hard wood used for masts and spars.



[Sinhalese p
 and Granger 2002, p. 5). According to according to
prep.
1. As stated or indicated by; on the authority of: according to historians.

2. In keeping with: according to instructions.

3.
 Sharpe (1964), the measure of portfolio performance management is defined as being the return in excess of risk free rate divided by the standard deviation. The Sharpe measure incorrectly penalizes the occasionally high returns. To this consideration, Markowitz (1959) advances the notion of the "semi-variance". The underlying idea consists in taking in account only square returns below the mean return. However, this notion didn't know a big success among portfolio managers.

A. Absolute and Squared Returns As Volatility Proxies

As mentioned previously, volatility is often estimated through a sample standard deviation. Researchers have pointed out methods for volatility estimation that are designed to exploit or to attenuate To reduce the force or severity; to lessen a relationship or connection between two objects.

In Criminal Procedure, the relationship between an illegal search and a confession may be sufficiently attenuated as to remove the confession from the protection afforded by the
 the influence of extreme values. Ding, Granger and Engle suggest measuring volatility directly from absolute returns. Indeed, Davidian and Cornell (1987) show that absolute returns volatility is more robust against asymmetry Asymmetry

A lack of equivalence between two things, such as the unequal tax treatment of interest expense and dividend payments.
 and non-normality. Some empirical studies such as Taylor (1986), present evidence that absolute returns based models generate better volatility forecasts than models founded on squared returns. Given that volatility is a latent variable In statistics, Latent variables (as opposed to observable variables), are variables that are not directly observed but are rather inferred (through a mathematical model) from other variables that are observed and directly measured. , the actual volatility is usually estimated from a sample using [[sigma].sup.2] expression that presents some inaccuracies when the sample size is small. Before high frequency data becomes widely available, many researchers have resorted to using daily squared returns, computed from closing prices as daily proxy of volatility.

B. Defining and Measuring Long Memory

According to Ding and Granger (1996), a series is said to have a long-memory if it displays a slowly declining autocorrelation function (ACF (Advanced Communications Function) An earlier official product line name for IBM SNA programs, such as VTAM (ACF/VTAM) and NCP (ACF/NCP).

ACF - Advanced Communications Function
) and an infinite spectrum at zero frequency. Specifically, the series [{[y.sub.t]}.sup.[infinity].sub.t=0] is said to be a stationary long-memory process if the ACF, [rho](k) behaves as,

[rho](k) [approximately equal to] [[absolute value of k].sup.2d-1] as [absolute value k] [right arrow] [infinity] (1)

where 0 < d < 0.5 0 and c is some positive constant. The left-hand side left-hand side nizquierda

left-hand side left nlinke Seite f

left-hand side nlato or
 and the right-hand side in equation (1) tends to 1 as k [right arrow] [infinity]. The ACF in (1) displays a very slow rate of decay to zero as k goes to infinity and [[summation].sup.[infinity].sub.k=-[infinity]] [absolute value of [rho](k)] = [infinity]. This slow rate of decay can be contrasted with ARMA processes, which have an exponential rate of decay, and satisfy the following bound,

[absolute value of [rho](k)] [less than or equal to] [ba.sup.k], 0 < b < [infinity], 0 < a < 1. (2)

And consequently, [[summation].sup.[infinity].sub.k=-[infinity]] [absolute value of [rho](k)] = [infinity]. A process that satisfies (2) is termed short-memory. Equivalently, long-memory can be defined as a spectrum that goes to infinity at the origin. This is,

f([omega]) [approximately equal to] c[[omega].sup.-2d] as w [right arrow] 0 (3)

A simple example of long-memory is the fractionally integrated noise process, I(d), with 0 < d < 1. Which is,

[(1 - L).sup.d] [y.sub.t] = [u.sub.t] (4)

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 [u.sub.t] ~ iid(0, [[sigma].sup.2]). This model includes the traditional extremes of a stationary process, I(0) and a nonstationary process I(1) . The fractional difference operator [(1 - L).sup.d] is well defined for a fractional d and the ACF of this process displays a hyperbolic hy·per·bol·ic   also hy·per·bol·i·cal
adj.
1. Of, relating to, or employing hyperbole.

2. Mathematics
a. Of, relating to, or having the form of a hyperbola.

b.
 decay consistent with equation (1). A model that incorporates the fractional differencing operator is a natural starting point Noun 1. starting point - earliest limiting point
terminus a quo

commencement, get-go, offset, outset, showtime, starting time, beginning, start, kickoff, first - the time at which something is supposed to begin; "they got an early start"; "she knew from the
 to capture long-memory. This is the underlying idea of the ARFIMA ARFIMA Autoregressive Fractionally Integrated Moving Average (econometrics)  and FIGARCH class of processes. In practice we must resort to estimating the ACF with usual sample quantities

[??](k) = 1/T [[summation].sup.T.sub.t=k+1]([y.sub.t] - [bar.[y.sub.t]])([y.sub.t-k] - [bar.[y.sub.t]])/ 1/T [[summation].sup.T.sub.t=k+1][([y.sub.t] - [bar.[y.sub.t]]).sup.2] (5)

A second approach to measure the degree of long-memory has been to use semiparametric methods. This allows one to review the specific parametric form, which is misspecified and could lead to an inconsistent estimate of the long memory parameter. In this paper, we consider the most two frequently used estimators of long memory parameter d. The first is the Geweke and Porter-Hudak (1983) (henceforth GPH GPH Gallons Per Hour
GPH Gospel Publishing House (Pentecostal Christian publisher)
GPH Grams Per Hour
GPH Good Payment History
GPH Generalized Proportional Hazard(s)
GPH Gnome Phone
) estimator, based on a log-periodogram regression. Suppose [y.sub.0], [y.sub.1], ... [Y.sub.T-1] is the dataset and define the periodogram for the first m ordinates as,

[I.sub.j] = 1/2[pi]T [[absolute value of [T-1.summation over (t=0)] [y.sub.t] exp exp
abbr.
1. exponent

2. exponential
(i[[omega].sub.j]t)].sup.2] (6)

where [[omega].sub.j] = 2[pi]j/T, j = 1,2 ... m, and m is chosen positive integer integer: see number; number theory . The estimate of ([??]) can then be derived from linear regression Linear regression

A statistical technique for fitting a straight line to a set of data points.
 of log [I.sub.j] on a constant and the variable [X.sub.j] = log [absolute value of 2 sin([[omega].sub.j]/2)], which gives,

[??] = - [[summation].sup.m.sub.j=1] ([x.sub.j] - [bar.x]) log [I.sub.j]/2[[summation].sup.m.sub.j=1] ([x.sub.j] - [bar.x]) (7)

Robinson (1995a) provides formal proofs of consistency and asymptotic normality normality, in chemistry: see concentration.  for the Gauss case with -0.5 < d < 0.5. The asymptotic standard error is [pi] / [square root of 24m]. The bandwidth parameter m must converge infinitely with the sample size, but at a slower rate than [square root of F]. Clearly, a larger choice of m reduces the asymptotic standard error, but the bias may increase. The bandwidth parameter was set to (T) in Geweke and Porter-Hudack (1983). While Hurvich, Deo and Brodsky (1998) show the optimal rate to be O([T.sup.4/5]). Recently, Hurvich and Deo (1999) have shown that the GPH estimator is also valid for some non Gaussian time-series. Velasco (1999) has shown that consistency extends to 0.5 < d < l and asymptotic normality to 0.5 < d < 0.75. The other popular semiparametric estimator is due to Robinson (1995b). Essentially, this estimator is based on the log-periodogram and solves:

[??] = arg min R(d) (8)

R(d) = log (1/m [m.summation over (j=1)] [[omega].sup.2.sub.j] [I.sub.j]) - 2d/m [m.summation over (j=1)]) [[omega].sub.j] (9)

The estimator is asymptotically more efficient that the GPH estimator and consistency and asymptotic normality of [??] are available under weaker assumptions than for the Gaussian case. The asymptotic standard error for [??] is 1/(2[square root of m]). Robinson and Henry (1996) have shown that this estimator is valid in the presence of some forms of conditional heteroskedasticity

III. THE PRACTICAL CONSIDERATIONS

Previous studies of long-memory and fractional integration in time series are numerous. Barkoulas, Baum, and Oguz (1999a, b), studied the long run dynamics of long-term interest rates and currencies. Recent studies of stock prices include Cheung and Lai (1995), Lee and Robinson (1996), Andersson and Nydahl (1998). Batten bat·ten 1  
v. bat·tened, bat·ten·ing, bat·tens

v.intr.
1. To become fat.

2.
, Ellis, and Hogan (1999) dealt with credit spreads of bonds. Wilson and Okunev (1999) searched for long-term co-dependence between stock and property markets. While the results on the level of returns are mixed, there is general consensus that the unconditional distribution is non-normal and that there is long-memory process in squared and absolute returns. The following are some issues. Though not mutually exclusive Adj. 1. mutually exclusive - unable to be both true at the same time
contradictory

incompatible - not compatible; "incompatible personalities"; "incompatible colors"
, they are separated by headings for easier discussions:

A. Risk and Volatility

Standard deviation is a statistical measure of variability and it has been called the measure of investment risk in the finance literature. Balzer (1995) argues that standard deviation is a measure of uncertainty and it is only a candidate, among many others, for a risk measure. Markowitz (1959) and Murtagh (1995) found that portfolio selection based on semi-variance tend to produce better performance than those based on variance. A normal distribution is completely characterised by its first two statistical moments, namely, the mean and standard deviation. However, once nonlinearity is introduced, investment returns distribution is likely to become markedly skewed skewed

curve of a usually unimodal distribution with one tail drawn out more than the other and the median will lie above or below the mean.

skewed Epidemiology adjective Referring to an asymmetrical distribution of a population or of data
 away from a normal distribution. In such cases, higher order moments such as skewness Skewness

A statistical term used to describe a situation's asymmetry in relation to a normal distribution.

Notes:
A positive skew describes a distribution favoring the right tail, whereas a negative skew describes a distribution favoring the left tail.
 and kurtosis Kurtosis

A statistical measure used to describe the distribution of observed data around the mean.

Notes:
Used generally in the statistical field, it describes trends in charts.
 are required to specify the distribution. Standard deviation, in such a context, is far less meaningful measure of investment risk and does not seem to be a good proxy for risk. While recent developments are interested in the conditional volatility and long memory in squared and absolute returns, most practitioners continue to think in terms of unconditional variance and continue thus to work with unconditional Gaussian distribution in financial applications. Recent publications are drawing attention to the issue of distribution characteristics of market returns, especially in emerging markets, which cannot be summarized by a normal distribution (Bekaert et al., 1998).

B. Estimating and Forecasting Asset Prices

Earlier perception was that deseasonalised time series could be viewed as consisting of two components, namely, a stationary component and a non-stationary component. It is perhaps more appropriate to think of the series consisting of both a long and a short memory components. A semiparametric estimated can be the first step in building a parametric time series model as there is no restriction on the spectral density In statistical signal processing and physics, the spectral density, power spectral density, or energy spectral density is a positive real function of a frequency variable associated with a stationary stochastic process, or a deterministic function of time, which has  away from the origin. Fractional ARIMA or ARFIMA can be used in forecasting although of the debates on the relative merits of using this class of models is still inconclusive INCONCLUSIVE. What does not put an end to a thing. Inconclusive presumptions are those which may be overcome by opposing proof; for example, the law presumes that he who possesses personal property is the owner of it, but evidence is allowed to contradict this presumption, and show who is  (Hauser, Potscher, and Reschenhofer, 1999, and Andersson, 1998). Lower risk bounds and properties of confidence sets of so called ill-posed problems associated with long-memory parameters are also discussed in Potscher (1999). The paper casts doubts on the used statistical tests in some semiparametric models on the ground that a priori a priori

In epistemology, knowledge that is independent of all particular experiences, as opposed to a posteriori (or empirical) knowledge, which derives from experience.
 assumptions regarding the set of feasible data generating processes have to be imposed to achieve uniform convergence of the estimator.

C. Portfolio Allocation Strategy

The results of Porterba and Summers (1988) and Fama and French (1988) provided the evidence that stock prices are not truly random walk. Based on these observations, Samuelson (1992) has deduced on a rational basis that it is more appropriate to have more equity exposure with long investment horizon than with short horizon. Optimal portfolio choice under processes other than white noise can also suggest lightening up on stocks when they have risen above trend and loading up when they have fallen below trend. This coincides with the conventional wisdom that long-horizon investors can tolerate more risk and therefore gain higher mean returns. As one grows older, one should have less holding of equities and more assets with lower variance than equities. This argues for "market timing" asset allocation Asset Allocation

The process of dividing a portfolio among major asset categories such as bonds, stocks or cash. The purpose of asset allocation is to reduce risk by diversifying the portfolio.
 policy and against the use of "strategic" policy by buying and holding as implied by the random walk model. Then, there is the secondary issue of short-term versus long-horizon tactical asset allocation Tactical Asset Allocation (TAA)

Portfolio strategy that allows active departures from the normal asset mix according to specified objective measures of value. Often called active management. It involves forecasting asset returns, volatilities, and correlations.
. Persistence or a more stable market calls for buying and holding after market dips. This would likely to be a mid to long-horizon strategy in a market trending upwards. Whereas, in a market that exhibits antipersistence, asset prices tend to reverse their trend in the short term creating thus short-term trading opportunities. It is unclear, taking transaction costs into account, whether trading the assets would yield higher risk adjusted returns. This is an area of research that may be of interest to practitioners.

D. Diversification and Fractional Cointegration

If assets are not close substitutes for each other, one can reduce risk by holding such substitutable assets in the portfolio. However, if the assets exhibit long-term relationship (e.g., to be co-integrated over the long-term), then there may be little gain in terms of risk reduction by holding such assets jointly in the portfolio. The fording of fractional cointegration implies the existence of long-term co-dependencies, thus reducing the attractiveness of diversification strategy as a risk reduction technique. Furthermore, portfolio diversification decisions in the case of strategic asset allocation Strategic Asset Allocation

A portfolio strategy that involves periodically rebalancing the portfolio in order to maintain a long-term goal for asset allocation.

Notes:
At the inception of the portfolio, a "base policy mix" is established based on expected returns.
 may become extremely sensitive to the investment horizon if long-memory is present. As Cheung and Lai (1995) and Wilson and Okunev (1999) have noted, there may be diversification benefits in the short and medium term, but not if the assets are held together over the long term naturally if long-memory is present.

E. Multifractal Model of Asset Returns and FIGARCH

The recently developed multifractal model of asset returns (henceforth MMAR MMAR Marijuana Medical Access Regulations
MMAR Mighty Morphin Alien Rangers (TV Show) 
) of Mandelbrot, Fisher and Calvet (1997) and FIGARCH process of Baillie, Bollerslev, and Mikkelsen (1996) incorporate long-memory and thick-tailed unconditional distribution. These models account for most observed empirical characteristics of financial time series, which show up as long tails relative to the Gaussian distribution and long-memory in the volatility (absolute return). The MMAR also incorporates scale-consistency, in the sense that a well-defined scaling rule relates return over different sampling intervals.

F. Stock Market Weak Form Efficiency

A time series that exhibits long memory process violates the weak form of efficient market hypothesis Efficient Market Hypothesis

States that all relevant information is fully and immediately reflected in a security's market price, thereby assuming that an investor will obtain an equilibrium rate of return.
 developed by Fama (1970); it states that the information in historical prices or returns is not useful or relevant in achieving excess returns. Consequently the hypothesis that prices or returns move randomly (random walk hypothesis The random walk hypothesis is a financial theory stating that stock market prices evolve according to a random walk and thus the prices of the stock market cannot be predicted. It has been described as 'jibing' with the efficient market hypothesis. ) is rejected.

IV. TUNISIAN STOCK MARKET OVERVIEW

A. The Main Reform Measures Concerning the TSE

1. Fiscal regime for holdings

Any company listed on the stock exchange and holding, directly or indirectly, at least 95% of capital in other companies can, as the parent company, opt for tax assessment on the basis of the combined earnings which. They should priorly be subject to corporate tax law, they must have the same accounting year opening and closing dates and be both established in Tunisia [see Note 1]. Tax incentives to companies which open their capital to the public were initially granted for a period of three years starting from January 1999, in the form of a reduced tax rate from 35 to 20%. This was extended for an additional period of three years starting from February 2002, with a view to encourage companies to be more transparent and also mobilising public savings by increasing the range of offerings and this by posting new stocks on the stock market.

2. Amendment of financial market council

This amendment supports greater transparency in public calls for savings by requiring that companies, seeking this kind of funding, to provide a more complete and reliable information. Thus, companies will have to provide to the Financial Market Council (henceforth, CMF CMF Christian Medical Fellowship
CMF Compressed Mortality File
CMF Content Management Framework
CMF Council of Michigan Foundations
CMF Congressional Management Foundation (Washington DC, USA)
CMF Code Monétaire et Financier
) and to shareholders the required information. To encourage new issues and transactions on the financial market, commissions to the CMF and the TSE were reduced. Previously calculated on the basis of the amount of the issue, commissions to he CMF are henceforth set at 0.2% of the nominal value Nominal Value

The stated value of an issued security that remains fixed, as opposed to its market value, which fluctuates.

Notes:
When referring to fixed-income securities, the nominal value is also the face value.
 of the issue.

B. Tunisian Stock Exchange Trends

TSE sent a higher level of public securities and a greater volume of transactions for the second straight year. But no new companies were posted on the stock exchange in 2000; despite larger fiscal incentives [see Note 2] that encourage new companies, already posted, to open their capital to the public. The CMF published regulations concerning public call for savings, which specify conditions, procedures and responsibilities of stockbrokers and companies issuing securities through public calls for savings. Concerning the official quotation, stock market activity was characterised by two distinct phases. Over the first nine months of the year, there is sustained demand for securities, especially for active stocks. Volume on stock market picked up in the light of figures of 1999 and the first half of 2000 concerning posted companies, dividend distribution and 13 capital increases operations. Total profits posted by listed companies on the basis of 1999 activity were up in 2000 by 14%, while dividends per share Dividends per share

Dividend paid for the past 12 months divided by the number of common shares outstanding, as reported by a company. The number of shares often is determined by a weighted average of shares outstanding over the reporting term.
 increased by an average 16%. But despite the overall improvement in distributed profit, the average market price earning ratio (PER) indicating the time required to recover investment was up from 13 in 1999 to 16 in 2000, tied to the higher cost of stock exchange quotations. The same forces that marked trading also accounted for an improvement in the securities ration ration

a fixed allowance of total feed for an animal for one day. Usually specifies the individual ingredients and their amounts and the amounts of the specific nutriments such as carbohydrate, fiber, individual minerals and vitamins.
 rate, which reached 23.6% vs. 16.7% in 1999 and 9.7% in 1998. Likewise, the average market liquidity rate was up slightly from 46% in 1999 to 51% in 2000. But trade remained insufficiently diversified, concentrating on a limited number of stocks: almost two thirds of total transactions involved just 10 stocks. In 2001, stock market quotations were marked by a process to adjust stock prices which had increased significantly during the last two years; and by weak demand for securities which sought mainly new issues made that year. Lack of confidence on the part of investors was the reason behind low demand, despite the favourable financial results published by listed companies; this became even more complicated, during the last quarter after the events of September 11th. Companies listed on the TSE increased from 42 at the end of 2000 to 45 at the end of 2001. The new members were included by public sale and by public subscription to capital increase transactions. During 2003, financial market activity showed timid timid,
adj in Chinese medicine, pertaining to inadequate energy needed to face and overcome obstacles.
 improvement, with a slight increase in the TUNINDEX and BVMT BVMT Brief Visual Memory Test  indexes and a drop in the volume of issues by public call for savings and transactions on quotations. Concerning the stock market activity, it was characterized by gradual recovery starting in the third quarter, as seen in higher prices for key stocks or for strong market capitalisation Noun 1. market capitalisation - an estimation of the value of a business that is obtained by multiplying the number of shares outstanding by the current price of a share
market capitalization
 ones. This upward trend was influenced in particular by improved national economic conditions, the 87.5 base point drop in the Central Bank of Tunisia's key rate, and heightened confidence on the part of operators, particularly the return of foreign investors. With no new entries on the market, the number of companies quoted on the stock exchange dropped from 46 in 2002 to 45 in 2003 [see Note 3]. The volume of transactions on the market fell by 225 105 MTD MTD Mounted
MTD Maximum Tolerated Dose
MTD Memory Technology Device
MTD Month To-Date
MTD Methadone (drug screening)
MTD motion to dismiss (legal)
MtD Mountain Dew
MTD Memory Technology Driver
 (31%) in 2003 to 238 MTD, an average daily volume under a million dinars, compared to 1.4 MTD in 2002. Some 12.9 million securities were transacted in 2003, down from 17 million in 2002, denoting a drop of 24.2%. Exchange of securities and transacted capital did not show much diversity, focusing on a limited number of stocks. Six stocks accounted for more than 60% of total capital transacted in 2003. Sector-related breakdown of traded stocks showed a 34% share for the banking sector in 2003, down from 38% in 2002. The share of the industrial sector also decreased, from 38% in 2002 to 29% in 2003. But the share of the services sector increased in 2003 to 27%, up from 16% in 2002.

V. MODELING THE LONG MEMORY OF THE VOLATILITY

Traditionally, the time series econometrics econometrics, technique of economic analysis that expresses economic theory in terms of mathematical relationships and then tests it empirically through statistical research.  centred itself around an alternative: the presence of a unit root, indicating a nonstationarity of the set, on the one hand, and the absence of such a unit root indicating that the set is stationary. On the other hand, these two cases correspond to cases of processes of short memory of ARIMA (p,d,q) and ARMA(p,q). These classic modeling doesn't take in account the intermediate cases to know the existence of a fractional integration parameter. However, the presence of such a coefficient no whole is especially interesting since it permits to characterize processes of long memory. These processes, called ARFIMA, have been introduced by Granger and Joyeux (1980) and Hosking (1981). They present the interest to take account at a time of the short-term behaviour of the set through autoregressive and moving average and the behaviour of long term by means of the fractional integration parameter. The ARFIMA (p, d, q) process can be defined as follows:

[PHI phi
n.
Symbol The 21st letter of the Greek alphabet.


PHI,
n See health information, protected.
](L)[(I - L).sup.d][y.sub.t] = [THETA](L)[[epsilon].sub.t] (10)

where, [PHI](L) and [THETA](L) are lag polynomials of p and q respectively. [[epsilon].sub.t] is a White noise process, and

[(I - L).sup.d] = 1 - dL - d(1 - d)/2! [L.sup.2] - d(1 - d)(2 - d)/3! [L.sup.3] - ...

ARFIMA (p,d,q) processes are stationary and inversible when d [member]]- 1/2,1/2[ and d [not equal ] 0.

A. Short and Long Term Memory and FIGARCH Processes

Considering a possible fractional integration of the conditional variance has been evoked initially by Ding and Granger (1996) and Ding, Granger and Engle (1993). Positively, FIGARCH processes have been introduced by Baillie, Bollerslev and Mikkelsen (1996). The starting point is a GARCH (p,q) process. It can be written as follows:

[[sigma].sup.2.sub.t] = [[alpha].sub.0] [q.summation over (i=1)] [[alpha].sub.i] [[epsilon].sup.2.sub.t-1] + + [p.summation over (j=1)] [[beta].sub.i] [[sigma].sup.2.sub.t-j] = [[alpha].sub.0] + [alpha](L)[[epsilon].sup.2.sub.L] + [beta](L)[[sigma].sup.2.sub.t] (11)

where [[alpha].sup.2] is the conditional variance; [[alpha].sup.0] > 0; [[alpha].sub.i] [greater than or equal to] 0; [[beta].sub.j] [greater than or equal to] 0, i = 1, ..., q. GARCH(p,q) process are short memory processes since the effect of a shock on the conditional variance decreases at an exponential rate. GARCH(p,q) can be also written as follows:

[1 - [alpha](L) - [beta](L)][[epsilon].sup.2.sub.t] = [[alpha].sub.0] + [1 - [beta](L)] [[mu].sub.t] (12)

Consequently, when the lag polynomial polynomial, mathematical expression which is a finite sum, each term being a constant times a product of one or more variables raised to powers. With only one variable the general form of a polynomial is a0xn+a  [1 - [alpha](L) - [beta](L)] contains a unit root, the GARCH process becomes an integrated GARCH process, denoted as IGARCH IGARCH Integrated Generalized Autoregressive Conditional Heteroskedasticity (p,q)). MARCH (p,q) process can be written as:

[PHI](L) = (1 - L)[[epsilon].sup.2.sub.t] = [[alpha].sub.0] + [1 - [beta](L)] [[mu].sub.t]

with [PHI](L) = [1 - [alpha](L) - [beta](L)[(1- L).sup.-1] (13)

FIGARCH processes constitute an alternative between GARCH processes and IGARCH processes and result with the equation (4) by replacing the operator (1- L) by the operator [(1- L).sup.d], where d is the fractional integration parameter. A FIGARCH process can be written as follows:

[PHI](L)[(1 - L).sup.d][[epsilon].sup.2.sub.t] = [[alpha].sub.0] + [1 - [beta](L)] [[mu].sub.t] (14)

Roots of [PHI](L) and [1 - [beta](L)] polynomials being outside the unit circle. Thus, if d=0, FIGARCH(p,d,q) process will be reduced to a GARCH(p,q). if d=1, FIGARCH process will be an IGARCH. By replacing [[mu].sub.t] by its value according to [[sigma].sup.2.sub.t], one can write equation (5) as follows:

[1 - [beta](L)][[sigma].sup.2.sub.t] = [[alpha].sub.0] + [1 - [beta](L) - [PHI](L)[(1 - L).sup.d]][[epsilon].sup.2.sub.t] (15)

The variance equation is then given by:

[[sigma].sup.2.sub.t] = [[alpha].sub.0] [[1 - [beta](1)].sup.-1] + [lambda](L)[[epsilon].sup.2.sub.t] with [lambda](L) = [1 - [[1 - [beta](L)].sup.-1][PHI](L)[(1 - L).sup.d]] (16)

= [[lambda].sub.1]L + [[lambda].sub.2]L + ... and [[lambda].sub.k] [greater than or equal to] 0 et k = 1, 2, ..., n

Baillie, Bollerslev et Mikkelsen (1996) note that the effects of a shock on the conditional variance of FIGARCH(p,d,q) decreases at an hyperbolic rate when 0 [less than or equal to] d < 1.

B. Data and Statistical Distribution

Our empirical investigation is conducted using daily returns of two Tunisian stock indexes (IBVMT [see Note 4] and TUNINDEX [see Note 5]). The data cover the period (1997/12/31- 2004/4/16) and totalling 1593 observations. Daily returns are calculated for the two indexes as continuously returns at time t ; [r.sub.i,t]. In other words Adv. 1. in other words - otherwise stated; "in other words, we are broke"
put differently
, as the natural log difference in the closing market index Pt between two days as shown below: [r.sub.i,t] =100Log([P.sub.t] / [P.sub.t-1]). Results reported in Table 2 call the following commentaries:

1. Mean returns of the BBVMT are the highest compared to the TUNINDEX. According to the t-statistics, only BBVMT mean returns are significantly different from zero at 5% significant level. Medians' returns are positive and confirm the same ranking of the indices, implying skewed series with departure from normality.

2. It is evident that the two indices returns are volatile. This has been confirmed by ARCH test where the null hypothesis null hypothesis,
n theoretical assumption that a given therapy will have results not statistically different from another treatment.

null hypothesis,
n
 of returns that are homoscedastistic is rejected at 1% significance level. There is evidence of heteroscedasticity in the daily and weekly two indices and for the frequencies. In other words, the BVMT and TUNINDEX returns exhibit clustering volatility and that there is a tendency for large (small) asset price changes to be followed by other large (small) price changes of either sign and tend to be time dependent.

3. Indices' returns display significant positive skewness where the null hypothesis of skewness coefficients conforming to the normal distribution value of zero is rejected. This result is in compliance with means greater than the medians in (1).

4. The null hypothesis of kurtosis coefficients conforming to the normal distribution value of three is rejected at five percent significance level for the BVMT and TUNINDEX weekly and daily returns. Thus, the returns of both indices are leptokurtic and their distributions have thicker (fatter) tails than that of a normal distribution.

5. Results of both (3) and (4) have been confirmed by rejecting the null hypothesis of the bivariate bi·var·i·ate  
adj.
Mathematics Having two variables: bivariate binomial distribution.

Adj. 1.
 Jarque-Bera test In statistics, the Jarque-Bera test is a goodness-of-fit measure of departure from normality, based on the sample kurtosis and skewness. The test statistic JB is defined as

 for unconditional normal distribution of the two stock market weekly and daily index returns.

6. With respect to Dickey-Fuller and Phillips-Perron unit root statistics, the null hypothesis for both tests whether indices returns, using t-statistics, have unit root is rejected in favour of the alternative that the four series are trend stationary process with a degree of predictability.

7. In sum, the BVMT and TUNINDEX weekly and daily returns tend to be characterized by positive skewness, excess kurtosis Excess kurtosis

Kurtosis measures the "fatness" of the tails of a distribution. Positive excess kurtosis means that distribution has fatter tails than a normal distribution. Fat tails means there is a higher than normal probability of big positive and negative returns realizations.
 and departure from normality. The two indexes, also, display a degree of heteroscedasticity. The findings are in conformation con·for·ma·tion
n.
One of the spatial arrangements of atoms in a molecule that can come about through free rotation of the atoms about a single chemical bond.
 with other market indexes and consistent with several other empirical studies [see Note 6] in which emerging markets returns depart from normality and the null hypothesis for a random walk is rejected.

VI. FIGARCH MODELING

Before estimating FIGARCH processes, we proceed to the application of the modified R/S R/S Remote Sensing
R/S Rally Sport
R/S Respectfully Submit
R/S Report of Survey
R/S Route Sheet
R/S Reentry System
R/S Revision Segment
R/S Rationalization & Standardization
R/S Regulatory or Safety (automotive requirements) 
 test (Lo (1991) in order to detect the presence, if any, of long-range memory in Tunisian stock market volatility series. Let us simply recall that the limiting distribution of the modified R/S statistic is known and it is thus possible to test the null hypothesis of short-term memory against the alternative of long-term memory. The critical values of this statistic have been tabulated by Lo (1991). The author demonstrated that this statistic was not robust to short-range dependence, and proposed the following one:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII ASCII or American Standard Code for Information Interchange, a set of codes used to represent letters, numbers, a few symbols, and control characters. Originally designed for teletype operations, it has found wide application in computers. ]

which consists of replacing the variance by the HAC HAC Housing Assistance Council
HAC Hill-Start Assist Control (automobiles)
HAC Hearing Aid Compatible
HAC Havre Athletic Club (Le Havre, France)
HAc Acetic Acid
HAC Honourable Artillery Company
 variance estimator in the denominator of the statistic. If q=0, Lo's statistic R/S reduces to Hurst's statistic. Unlike spectral analysis which detects periodic cycles in a series, the R/S analysis has been advocated by Mandelbrot for detecting non periodic cycles. Under the null hypothesis of no long-memory, the statistic [T.sup.-1/2] [Q.sub.n] converges to a distribution equal to the range of a Brownian bridge A Brownian bridge is a continuous-time stochastic process whose probability distribution is the conditional probability distribution of a Wiener process B(t) (a mathematical model of Brownian motion) given the condition that B(0) = B  on the unit interval For the data transmission signaling interval, see .

In mathematics, the unit interval is the interval [0,1], that is the set of all real numbers x such that zero is less than or equal to x and x is less than or equal to one.
:

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

where [W.sup.0](t) is a Brownian bridge defined as [W.sup.0](t)= W(t)-tW(1), W(t) being the standard Brownian motion Brownian motion

Any of various physical phenomena in which some quantity is constantly undergoing small, random fluctuations. It was named for Robert Brown, who was investigating the fertilization process of flowers in 1827 when he noticed a “rapid oscillatory
. The distribution function is given in Siddiqui (1976), and is tabulated in Lo (1991). This statistic is extremely sensitive to the order of truncation q but there are no statistical criteria for choosing q in the framework of this statistic. Andrews (1991) rule gives mixed results. If q is too small, this estimator does not account for the autocorrelation of the process, while if q is too large, it accounts for any form of autocorrelation and the power of this test tends to its size. Given that the power of a useful test should be greater than its size; this statistic is not very helpful. For that reason, Teverovsky, Taqqu and Willinger (1999) suggest to use this statistic with other tests. Since there is no data driven guidance for the choice of this parameter, we consider the default values for q = 5, 10, 25, 50. Results reported in Table 3 indicate that the two volatility series display a strong dependent structure. To verify this result and to take into account long-term property, we estimate FIGARCH process.

A. Geweke Porter-Hudack (1983) Tests

In this respect, two procedures have been retained: the GPH method and the maximum likelihood technique. The GPH method is founded on the behaviour of the spectral density around low frequencies. It is a two-step technique since one estimate in the first stage the fractional integration parameter d and, in the second stage the parameter of the GARCH model. Concerning the maximum likelihood procedure (Sowel (1992)), it is a one-step procedure: all the parameters of the FIGARCH(p,d,q) specification are estimated simultaneously. The GPH estimation of FIGARCH processes are reported in table below. Let us recall that the function g(T) used in the spectral technique, corresponds to the number of periodogram ordinates. T is the number of observations. In order to examine the stability of the estimation when the number of periodogram ordinates vary, we have chosen different values: [T.sup.0.45], [T.sup.0.5], [T.sup.0.55] and [T.sup.0.8].

Results obtained using the spectral technique, emphasize the presence of long memory for the TUNINDEX stock returns. For the IBVMT volatility, the presence of a long-term structure depends on the number of periodogram ordinates retained. It will be also noted that the fractional integration parameter is positive in all cases. Judged by standard significance levels, [??] is statistically very different from both zero and one. Concerning, the exact maximum likelihood method, we observe, according the SIC model selection criteria, the presence of long-term dependence structure for the IBVMT volatility.

B. Lobato and Robinson (1998) Test

Lobato and Robinson (1998) nonparametric test for I(0) against I(d) is also based on the approximation approximation /ap·prox·i·ma·tion/ (ah-prok?si-ma´shun)
1. the act or process of bringing into proximity or apposition.

2. a numerical value of limited accuracy.
 of the spectrum of a long-memory process. In the univariate case, the t statistic t statistic, t distribution

the statistical distribution of the ratio of the sample mean to its sample standard deviation for a normal random variable with zero mean.
 is equal to:

t = [m.sup.1/2] [[??].sub.1]/[[??].sub.0] with [[??].sub.k][m.sup.-1] [m.summation over (j=1)] [v.sup.k.sub.j] ([[lambda].sub.j]) and [v.sub.j]=ln(j) - 1/m [m.summation over (i=1)] ln(i)

where I([lambda]) = [(2[pi]T).sup.-1] [absolute value of [[[summation].sup.T.sub.t=1] y [e.sup.it[lambda]]].sup.2] is the periodogram estimated for a degenerate band of Fourier frequencies [[lambda].sub.j] = 2[pi]j/T, j = 1, ..., m [less than or equal to] [T/2], where m is a bandwidth parameter. Under the null hypothesis of a I(0) time series, the t statistic is asymptotically normally distributed. This two sided test is of interest as it allows to discriminate between d > 0 and: d < 0 if the t statistic is in the lower fractile of the standardized normal distribution, the series exhibits long-memory whilst if the series is in the upper fractile of that distribution, the series is antipersistent. The default bandwidth suggested by Lobato and Robinson is used. The results are displayed in Table 5. The first column contains the value of the bandwidth parameter while the second column displays the corresponding statistic. In the first line, the Lobato-Robinson statistic is evaluated by using this default bandwidth. As t is negative and in the lower tail of the standard normal distribution, there is evidence on long-memory volatility. Semiparametric test for I(0) of a time series against fractional alternatives, (i.e., long-memory and antipersistence). Let us recall that it is a semiparametric test in the sense that it does not depend on a specific parametric form of the spectrum in the neighbourhood of the zero frequency. Concerning the parameter specifying the number of harmonic frequencies around zero to be considered, we use the bandwidth given in Lobato and Robinson. If the value of the test is in the lower tail of the standard normal distribution, the null hypothesis of I(0) is rejected against the alternative that the series displays long-memory. If the value of the test is in the upper tail of the standard normal distribution, the null hypothesis I(0) is rejected against the alternative that the series is antipersistent. As it is shown in the Table 5, the t statistic is negative and it is in lower tail of the standard normal distribution, we can conclude to the presence of long-memory in BVMT and TUNINDEX time series volatility.

C. Lo (1991) Tests

Results in Table 6 indicate that only the BVMT daily and absolute returns display long-term memory for different weights suggested by Newey and West (1987). This result confirms the conclusions issued from Lobato and Robinson (1995b). For the TUNINDEX series, a short dependent structure seems to be present in volatility series. In order to verify this result and take into account this long-term property, we apply the Robinson and Whittle semi-parametric estimator procedures and we estimate FIGARCH processes.

D. Robinson (1994b) Tests

The Robinson (1994b) averaged periodogram estimator is defined by: [??] = 1/2 - ln([??](q[[lambda].sub.m])/[??]([[lambda].sub.m])/2ln(q), where [??]([lambda]) is the average periodogram [??]([lambda]) = 2[pi]/n [n[lambda]/2[pi].summation over (j=1)]. By construction, the estimated parameter [??] is < 1/2 , i.e., is in the stationarity range. This estimator has the following asymptotic distribution In mathematics and statistics, an asymptotic distribution is a hypothetical distribution that is in a sense the "limiting" distribution of a sequence of distributions. A distribution is an ordered set of random variables

Zi


for i
 if [??] < 1/4, [square root of m]([??] - d) [right arrow] (0, [[pi].sup.2]/24).

The results of Robinson tests are reported in the Table 7. The Robinson procedure gives the semiparametric average periodogram estimator of the degree of long memory of a time series. The third column in the Table 8 designed the optional argument that is a strictly positive constant q, which is also strictly less than one. The second column designed the bandwidth vector m. By default q is set to 0.5 and 0.7 and the bandwidth vector is equal to m = n/4, n/8, n/ 16. If q and m contain several elements, the estimator is evaluated for all the combinations of q and m. The first column in the table designed the estimated degree of long-memory. Concerning the BVMT daily absolute returns, the results of the estimated degree of long-term memory range from 0.2310 to 0.2672 for the different values of q and bandwidth vector. For weekly absolute returns, the d parameter ranges from 0.0583 to 0.1940. Theses results indicate evidence that the BVMT volatility exhibit a long-range dependency phenomenon. The fractional differencing parameter is positive and d [member of] [0;0.5] it indicates the presence of a long-range positive dependence in the conditional variance. Quite similar results are obtained for the daily and weekly absolute returns.

E. Whittle Semiparametric Gaussian Estimator

The Whittle semiparametric Gaussian estimator of the degree of long memory of a time series is based on the Whittle estimator. The first argument is the series; the second argument is the vector of bandwidths, i.e., the number of frequencies after zero to be considered. By default, the bandwidth vector m = n/4, n/8, n/16, where n is the sample size. This table gives the estimated parameter d, with the number of frequencies considered. The obtained results emphasize the presence of a long-term dependence structure for all the series of volatility. Moreover, one notes a relative stability of the fractional integration parameter value for the BVMT daily volatility for the different sizes of the bandwidth vector. The results indicate also, for all the volatility series, a positive fractional integration parameter. So, all the series are characterized by a long-range positive dependence in the conditional variance. In order to verify this result and take into account this long-term property, we estimate FIGARCH processes.

F. The FIGARCH Process

The empirical investigation is conducted using, parsimoniously, FIGARCH(1,d,1) to specify the long memory process in Tunisian stock market volatility.

The results in Table 9 provide the following observations:

1. For the IBVMT absolute daily returns, the results exhibit fractional dynamics with long memory features. The null hypothesis ([H.sub.0] : d = 0) has been rejected in favour of d-value which is statistically significantly greater than zero at 1% significant level. The fractional differencing parameter value recorded approximately 0.4645 and it is in conformation with that of previously preliminary tests. There's also evidence that the BVMT volatility exhibit a long-range dependency phenomenon. The fractional differencing parameter is equal to 0.12115 and it is statistically significant at 1% significant level. The process is considered to be long-range positive dependence in the conditional variance as d [member of] [0;0.5].

2. Concerning the TUNINDEX daily volatility, the obtained results show the significance of both [[alpha].sub.1] and [[beta].sub.1] provided evidence that conditional volatility is time variant and there is volatility clustering In finance, volatility clustering refers to the observation, as noted by Mandelbrot, that "large changes tend to be followed by large changes, of either sign, and small changes tend to be followed by small changes.  effects. The results confirm that there is a tendency for shocks to persist, with large (small) innovations followed by similar ones. The estimation results of the FIGARCH (1,d,1) provide evidence that the TUNINDEX daily volatility exhibits fractional dynamics. The estimated d-value is statistically significantly greater than zero and indicates the presence of positive persistence phenomenon in the TUNINDEX volatility.

3. The results also provide evidence that the aggregation of short-memory process, could led to long memory feature, which is consistent with Robinson (1978), Taqqu et al. (1997), Chambers (1998), Cioczek-Georges and Mandelbrot (1995) findings. The evidence is consistent with number of emerging market characteristics.

4. As expected, the market adjusts slowly for the arrival of new information slowly which might be due to number of market structural reasons as the dominance of individual investors on trading activity who lack the equity culture and whose investment strategy is characterized by herd behaviour. The presence of nonsynchronous trading is probably due to large number of inactive stocks listed on the Tunisian Stock Exchange.

VII. CONCLUSION

The purpose of this paper was to study the long-range dependency of stock market volatility. More specifically, our object was to test the significant evidence for the presence of fractional integrated behaviour in the conditional variance of the Tunisian stock indexes. Thus, a new class of more flexible fractionally integrated GARCH (FIGARCH) models for characterizing the long run dependencies in the Tunisian stock market volatility was proposed. The investigation is conducted using the BVMT and TUNINDEX daily and weekly indexes during the period January 1998 till the end of April 2004. In this paper, strong evidence was uncovered that the conditional variance of the BVMT and TUNINDEX indexes is best modelled as a FIGARCH process. These findings of long memory component in the volatility processes of asset returns have important implications of many paradigms in modern financial theory. So, optimal portfolio allocations may become extremely sensitive to investment horizon if the volatility returns are long-range dependent. Similarly, optimal hedging decisions must take into account any such long-run dependency. Also, the assumption that the Tunisian Stock Market is weakly efficient is rejected due to long-range dependency in weekly and daily volatilities. This evidence is consistent with number of emerging market characteristics. A more formal and detailed empirical investigations of these issues on the Tunisian context would be important task for further research.

APPENDIX

[FIGURE 1 OMITTED]

[FIGURE 2 OMITTED]

[FIGURE 3 OMITTED]

[FIGURE 4 OMITTED]

[FIGURE 5 OMITTED]

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An estimation of a security's potential to suffer a decline in price if the market conditions turn bad.

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ENDNOTES

(1.) Note Law no 2000-98 of 25 December.

(2.) Under law no 99-92 of 17 august 1999.

(3.) BATAM Company was written off the stock market on 10 February 2003, as per decision of the market's governing council.

(4.) The IBVMT index evolution reflects the stock market average return. Are included in the reference sample all companies admitted in stock market, before it is adjusted on 31 March, 1998. The new reference sample limits itself to values of which the frequency of quotation is superior to 60%. The BVMT index has been published under its present shape on April first, 1998, with a base value of 465.77 on 31 March 1998.

(5.) It is a new stock market capitalization Market Capitalization

A measure of a public company's size. Market capitalization is the total dollar value of all outstanding shares. It's calculated by multiplying the number of shares times the current market price. This term is often referred to as market cap.
 index (base 1000 on 31 December 1997). It was initially published on first April 1998. Concerning its calculation, it is taken account of mean weighted return. The weight corresponds to the number of exchanged stocks. The base sample is composed of values admitted by their ordinary shares to stock market quotes and of which the living period in one of market quotes (primary or secondary market) it is of at least 6 months.

(6.) Mandelbort (1963) and Fama (1965) showed that unconditional distribution of security price changes to be leptokurtic, skewed and volatility clustered. Bekaert et al. (1998) provided evidence that 17 out of the 20 emerging markets examined their monthly returns had positive skewness and 19 out of 20 had excess kurtosis, so that normality was rejected for more than half of the countries.

(7.) Dickey and Fuller (1979) devised a procedure to formally test for the presence of unit root using three different regressions. In our case, the following regression with constant and trend is used to test for nonstationarity: [DELTA][y.sub.t] = [a.sub.0] + [gamma][y.sub.t-1] + [a.sub.1]t + [p.summation over (i=2)] [[beta].sub.t] [DELTA][y.sub.t-i] + [[epsilon].sub.t]. The null hypothesis is that [gamma] = 0 for stochastic nonstationary process.

(8.) Phillips-Perron nonparametric unit root tests were used because they allow for a general class of dependent and heterogeneously distributed innovations, contrary to other unit root tests (see Phillips and Perron, 1998).

Mondher Bellalah (a), Chaker Aloui (b), Ezzeddine Abaoub (c)

(a) THEMA, University de Cergy, and ISC (1) (Internet Systems Consortium, Redwood City, CA www.isc.org) An organization founded by Paul Vixie, Carl Malamud and Rick Adams in 1994 and later sponsored by UUNET and other Internet companies.  Group, Paris, 33 Boulevard du port, 9501 Cergy, France, France, Mondher.Bellalah@eco.u-cergyfr

(b) Faculty of Economics and Management Sciences of Tunis, Boulevard du 7 Novembre, Tunis- El Manar, Tunisia, Chaker.Aloui@fsegt.rnu.tn.

(c) Faculty of Economics and Management Sciences of Tunis, Boulevard du 7 Novembre, Tunis-EZManar, Tunisia, Ezzeddine.Abaoub@fsegt.rnu.tn
Table 1

Main Tunisian stock market indicators (1997-2004) (in MTND unless
otherwise indicated)

Description                         1997     1998     1999     2000

BVMT index in points (base 100
on 30 September, 1990, adjusted
on 31 march 1998 to 465.77         455.64   464.56   810.24   1424.91

TUNINDEX in points (base 1000
on 31 December 1997                1,000     917     1,193     1,443

Stock market capitalisation (a)    2,632    2,452    3,326     3,889

Stock market capitalisation/GDP
(in %)                              12.6     10.9     13.5     14.6

Number of listed companies           34       38       44       42 *

Overall volume of transaction       590      927      881      1 814

of witch: official
quotation (b)                       287      237      554       919

Rotation rate (in %) (a/b)           11       10       17       24

Liquidity rate (in %)                36       37       46       51

PER                                  12       10       13       16

Description                         2001     2002     2003

BVMT index in points (base 100
on 30 September, 1990, adjusted
on 31 march 1998 to 465.77         996.09   782.93   939.78

TUNINDEX in points (base 1000
on 31 December 1997                1,267    1,119    1,250

Stock market capitalisation (a)    3,275    2,842    2,976

Stock market capitalisation/GDP
(in %)                              11.4     9.5      9.2

Number of listed companies           45       46       45

Overall volume of transaction      1 204    1 006     948

of witch: official
quotation (b)                       508      343      238

Rotation rate (in %) (a/b)           16       12       8

Liquidity rate (in %)                49       42       33

PER                                  10       12       13

Table 2

Descriptive statistics

                                    Daily frequency

                                 IBVMT          TUNINDEX
                                returns          returns

Mean (%)                        4.89967          1.58515

t-statistic                     2.3365           1.3428

S. deviation (%)                83.6171          47.0108

Kurtosis                        5.00454          7.15012

Excess Kurtosis                 2.00454         4.015012

Skewness                       0.254762         0.639271

Jarque-Bera normality
test                          244.34 ***       435.43 ***

Augmented Dickey-
Fuller test [see Note 7]      -19.63 ***       -21.43 ***

Phillips-Perron unit root
test [see Note 8]             -26.39 ***       -27.01 ***

KPSS test                     0.66016 (3)      0.27769 (3)

ARCH- test                      231.358          306.345
                             Prob. (0.000)    Prob. (0.000)

Maximum                        4.000052         3.040505

Minimum                        -3.06502         -2.04465

Sample period                 31/12/1997       16/04/2004

Observation                      1590             1590

                                    Weekly frequency

                                 IBVMT          TUNINDEX
                                returns          returns

Mean (%)                       23.82330          7.97470

t-statistic                     2.0231           1.9781

S. deviation (%)               2.385634          1.81103

Kurtosis                       13.88606         53.62137

Excess Kurtosis                10.88606         50.62137

Skewness                       0.697379         0.2266645

Jarque-Bera normality
test                          345.32 ***       354.22 ***

Augmented Dickey-
Fuller test [see Note 7]      -10.589 ***      -12.613 ***

Phillips-Perron unit root
test [see Note 8]             -16.758 ***      -20.804 ***

KPSS test                     0.66431 (1)       0.136 (1)

ARCH- test                      73.067           66.005
                             Prob. (0.000)    Prob. (0.000)

Maximum

Minimum

Sample period                 31/12/1997       16/04/2004

Observation                       328              328

Note: The Jarque-Bera test for normality distributed as Chi-square
with 2 degrees of freedom. The critical value for the null hypothesis
of normal distribution is 5.99 at the 5% significance level. Higher
test values reject the null hypothesis. *** denotes significance at
1% level.

Table 3

Lo R /S modified test

                  BVMT

  Daily returns            Weekly returns

Order    [[??].sub.T]    Order    [[??].sub.T]
          statistic                statistic

    5      4.2912 *        5         1.6179
   10      3.6252 *       10         1.5630
   25      2.8189 *       25         1.4012
   50      2.3489 *       50         1.2843

              TUNINDEX

  Daily returns            Weekly returns

Order    [[??].sub.T]    Order    [[??].sub.T]
          statistic                statistic

  5        2.5101 *        5         1.1036
 10        2.3412 *       10         1.0553
 25        2.0516 *       25         1.0523
 50        1.9224 *       50         1.1748

Note: string vector containing the estimated statistic with its
corresponding order. If the estimated statistic is outside the
interval (0.809, 1.862), which is the 95 percent confidence interval
for no long-memory, a star symbol * is displayed in the third column.
The other critical values are in Lo's paper.

Table 4

GPH estimation of fractional integration parameter

g(T)               [T.sup.0.45]    [T.sup.0.5]

                               BVMT

Daily absolute          --           0.12343
  returns                            (3.034)
Weekly absolute       0.3452         0.3944
  returns            (2.087)         (2.056)

                             TUNINDEX

Daily absolute          --           0.0878
  returns                            (2.736)
Weekly absolute         --           0.0297
  returns                            (1.674)

g(T)               [T.sup.0.55]    [T.sup.0.8]

                               BVMT

Daily absolute        0.1147         0.1132
  returns            (2.8791)        (2.657)
Weekly absolute       0.3809           --
  returns            (3.453)

                             TUNINDEX

Daily absolute          --             --
  returns
Weekly absolute         --             --
  returns

T is the number of observations, g T the number of periodogram
ordinates, t-statistic of d are given into brackets. (-) non
significant.

Table 5

Lobato and Robinson (1998) tests

                      BVMT

Daily absolute            Weekly absolute
returns                   returns

Bandwidth     t stat.     Bandwidth    t stat.

 133 (a)      -14.30       22 (a)       -1.92
   150        -15.49        150         -4.93
   200        -18.28         --          --
   250        -19.25         --          --

                 TUNINDEX

Daily absolute            Weekly absolute
returns                   returns

Bandwidth     t stat.     Bandwidth    t.stat

 133 (a)       -4.05       19 (a)        0.34
   150         -4.32         150        -4.93
   200         -4.56         --          --
   250         -5.42         --          --

Notes: (a) Bandwidth given in Lobato and Robinson (1998).

Table 6

Lo (1991) tests

            BVMT                  TUNINDEX

            Daily      Weekly     Daily       Weekly
            absolute   absolute   absolute    absolute
            returns    returns    returns     returns

m = 5       2.1776     2.6179     0.41119     1.1036
m = 10      2.2963     2.5630     0.44367     1.0553
m = 25      2.6234     2.4012     0.57244     1.0523
m = 50      2.8841     2.2843     0.57726     1.1748

Table 7

Robinson (1994b) tests

                       BVMT
                                Weekly absolute
Daily absolute returns          returns

            Band                             Band
d           width       q          d         width      q

0.2672       250       0.5       0.0583       82       0.5
0.2427       250       0.7       0.1940       82       0.7
0.2380       500       0.5       0.0898       41       0.5
0.2310       500       0.7       0.0830       41       0.7
0.2419       750       0.5       0.1886       20       0.5
0.2546       750       0.7       0.0998       20       0.7

                      TUNINDEX

                                Weekly absolute
Daily absolute returns          returns

            Band                             Band
   d        Width       q          d         width      q

0.1236       250       0.5       0.2097       82       0.5
0.1180       250       0.7       0.2244       82       0.7
0.2240       500       0.5       0.0590       41       0.5
0.2338       500       0.7       0.0089       41       0.7
0.2255       750       0.5       0.0305       20       0.5
0.2201       750       0.7       0.0044       20       0.7

Table 8
Whittle Semi-parametric estimator of the degree of long memory of
daily and weekly absolute returns

                                 BVMT

 Daily absolute                  Weekly absolute
    returns                      returns

       d            Bandwidth       d        Bandwidth

     0.3342            50         0.0876        50
     0.3441            100        0.1197       100
     0.3171            150        0.2232       150

                    TUNINDEX

 Daily absolute                  Weekly absolute
    returns                      returns

       d            Bandwidth       d        Bandwidth

     0.1996            50         0.0441        50
     0.1841            100        0.1808       100
     0.1362            150        0.2839       150

Table 9

Estimates for FIGARCH (1,d,1) model for TSE weekly and daily
volatility Using Broyden, Fletcher, Goldfrab and Shanno (BFGS)
Maximization Method

                               BVMT index

                      Daily absolute      Weekly
                      returns             absolute
                                          returns

[[alpha].sub.0]       0.01055             0.00903
                      (-1.97711) **       (2.0112) **

[[alpha].sub.1]       0.87184             0.66121
                      (7.3629) ***        (5.3124) ***

[[beta].sub.1]        0.11832             0.34079
                      (1.0680)            (2.0123) **

l([theta])            851.841             546.125

d                     0.4645              0.12115
                      (6.35404) ***       (5.4432) ***

[[alpha].sub.1]
+ [[beta].sub.1]      0.9902              1.002

                              TUNINDEX

                      Daily absolute      Weekly
                      returns             absolute
                                          returns

[[alpha].sub.0]       0.02311             0.0121
                      (1.4561)            (1.1113)

[[alpha].sub.1]       0.42131             0.33427
                      (3.3211) **         (2.0278) **

[[beta].sub.1]        0.57669             0.66583
                      (4.3242) ***        (1.9902) **

l([theta])            243.121             311.342

d                     0.1996              0.0431
                      (4.3421) ***        (1.3211)

[[alpha].sub.1]       0.9980              1.0001
+ [[beta].sub.1]

*** Significant at 1 percent, ** significant at 5 percent,
* significant at 10 percent.
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Author:Bellalah, Mondher; Aloui, Chaker; Abaoub, Ezzeddine
Publication:International Journal of Business
Date:Jun 22, 2005
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