Trading volume, time-varying conditional volatility, and asymmetric volatility spillover in the Saudi stock market

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Abstract

Despite the well known importance of volatility–volume relationship, there is a paucity of research on this topic in emerging markets. We attempt to partially fill this gap by investigating volatility–volume relationship in the most important exchange market in the Middle East. We test the effect of trading volume on the persistence of the time-varying conditional volatility of returns in the Saudi stock market. Overall our results support the mixture of distribution hypothesis at the firm level. We also use two different proxies for information arrival, intra-day volatility, and overnight indicators. We find that these are good proxies for information and are important as contemporaneous volume in explaining conditional volatility. We also test for the volatility spillover direction between large- and small-cap portfolios. Our results show that the spillover effect is larger and statistically significant from large to small companies.

Introduction

The importance of trading volume and its impact on volatility of financial assets are well known in finance literature. Karpoff (1987) seminal paper summarizes the importance of this research area by presenting the following argument. First, the theory of the stock returns volatility–volume relationship provides insight into the structure of financial markets. It predicts that this relationship depends upon the rate of information flow to the market, information dissemination, market size, and the existence of short sale constraints. Second, the stock returns volatility–volume relationship has important implications for event studies that use a combination of price and volume data. Third, the relationship has important implications for the empirical distribution of speculative assets. In particular, the findings of the stock returns volatility–volume tests generally support the mixture of distributions hypothesis (MDH), which helps explain the observed kurtosis in empirical stock return distributions.

Despite the obvious importance of volatility–volume relationship, there is a paucity of research on this topic in emerging markets. We attempt to partially fill this gap by investigating volatility–volume relationship in the most important market in the Middle East, the Saudi stock market (SSM). The SSM already has by far the region's largest stock exchange. The exchange lists stocks with total market value of nearly $500 billion.1

We test the effect of trading volume on the persistence of the time-varying conditional volatility of returns in the SSM with the purpose of offering support to either the mixture of distributions hypothesis or the sequential information arrival hypothesis (SIAH). We utilize the generalized autoregressive conditional heteroskedasticity (GARCH(1, 1)) models to test for the persistence of return volatility without volume, with contemporaneous volume, and with lagged volume. Trading volume is measured as the number of shares traded during the day. In addition to volume, we use two different proxies for information arrival, intra-day volatility (IDV), and overnight indicators (ONI), which were presented by Gallo and Pacini (2000). The empirical tests are applied on the SSM index, five industry indices, and a sample of 15 individual firms.

This paper also tests the direction of the volatility spillover between large- and small-cap portfolios. The objective is to determine whether the volatility spillover direction between large and small firms is asymmetric in the SSM. We use a two-stage GARCH(1, 1) approach to test for spillover direction.

The contribution of this paper can be summarized as follows. First, this paper fills the gap in investigating volatility in the SSM despite its prominent importance in the region. According to the Arab Monetary Fund's annual report for the year ended December 2005, which provides statistics for all 15 Arab stock markets, the capitalization of the SSM represents 50% of the total market capitalization of these markets, while the value traded of the SSM represents 76.9% of the total stock value traded in all these markets. The report includes the markets of all Arab countries, namely, the Abu Dhabi Securities Market, the Amman Stock Exchange, the Bahrain Stock Exchange, the Beirut Stock Exchange, the Casablanca Stock Exchange, the Doha Stock Exchange, the Dubai Financial Market, the Egyptian Capital Market, the Kuwait Stock Exchange, the Muscat Securities Market, the Palestine Securities Exchange, the Saudi stock market, and the Tunis Stock Exchange.

Moreover, the SSM has become one of the leading emerging markets. According to statistics provided by the World Federation of Exchanges (WFE) for December 2005, the SSM was ranked 16th in terms of a market domestic capitalization of $650.18 billion, well ahead of Bombay Stock Exchange, Taiwan, Shanghai, Singapore, and many other historically world-leading stock exchanges. The market index gained over 40% for 2005, which followed six years of growth at an average annual rate of 38%. Market volume has also increased significantly. On average, market volume was worth over $4 billion a day in 2005 (Saudi Stock Exchange Reports, 2005).

Second, there are several characteristics of the SSM that differentiate it from other developed and emerging markets. In addition to the relatively large size of the market in the region and its strong development and growth, the behavior, structure, and size of the SSM differs in many ways from other markets. The SSM is a very large market in term of capitalization and trading volume, with a relatively small number of publicly traded companies (85). Relative to other markets, the breadth of this market is small while the capitalization and trading volume are relatively large; this makes it interesting to examine the effects of these specific characteristics on investors and accordingly on return behavior. Another aspect of the Saudi market that differentiates it from the structure of most developed markets is the lack of an options market, which in some studies has been found to affect the price and volatility of the underlying market (Cornard, 1989, St. Pierre, 1998). In addition, although many government-owned companies have gone public in recent years, the government still owns the majority shares of their stocks, which may impact the market return behavior.

Third, there are some inconsistent results in the literature on the relationship between market volatility and trading volume. For example, some studies find that the persistence of the GARCH effect disappears after including volume in the conditional variance (Lamoureux and Lastrapes, 1990), while others find that the GARCH effect does not completely disappear (Sharma et al., 1996, Kamath and Chusanachoti, 2000). In-between these two opposing views, some researchers find different results depending on the theory they use. For example, Darrat et al. (2003) does not find support for the MDH, but does find support for the SIAH for DJIA stocks. We extend the research in this area by offering an out-of-sample empirical test from a different market to the conditional and asymmetric volatility literature, which should help us to understand better the information transmission, volatility estimation, and pricing behavior in the SSM.

The remainder of this paper is as follows. Literature review is presented in Section 2. Section 3 presents the methodology employed in this study. The data and the empirical results are discussed in Section 4. Section 5 concludes the paper.

Section snippets

Literature review

The main theoretical foundation of studies on the relationship between trading volume and volatility is related to either the SIAH (sequential information arrival hypothesis) or the MDH (mixture of distribution hypothesis). The seminal study of Copeland (1976) assumes that traders receive new information in sequential random style; accordingly, he developed the SIAH. Starting at equilibrium, all traders possess the same set of information; traders then start to change their trading positions

Methodology

This study uses the GARCH model proposed by Bollerslev (1986). The GARCH model is an extension of the autoregressive conditional heteroskedasticity (ARCH) model (Engle, 1982) that allows conditional variance to change over time as a function of past error. To examine the effect of volume on stock returns volatility, the following GARCH(1, 1) model is employed:rt=β1+β2rt1+εtεt|Φt1N(0,ht)ht=α0+α1εt12+α2ht1+α3Vtwhere rt is the daily SSM market return measure, Vt is the daily volume, and β1, β2

Descriptive statistics

Empirical tests are applied to daily stock return and volume data over three levels: (1) the market level as measured by the Tadawul All Shares Index (TASI); (2) the industry level as measured by five industry indices, the Tadawul Banking Shares Index (TBSI), Tadawul Cement Shares Index (TBSI), Tadawul Agricultural Shares Index, Tadawul Industrial Shares Index (TISI), and Tadawul Service Shares Index (TSSI); and (3) the firm level as measured by the data of 15 individual companies listed in

Conclusion

This paper tests the persistence of return volatility in the SSM both with and without volume, with lagged volume, and with intra-day volatility and overnight indicators. We apply our models to the market indices, five industry indices, and 15 individual companies. The results show that the indices and sample firms of the SSM exhibit strong volatility persistence; however, when we include contemporaneous volume, the persistence vanishes at the firm level, indicating that the rate of information

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