Shock and volatility spillovers among equity sectors of the Gulf Arab stock markets

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Abstract

The major objectives of this study are twofold. The first objective is to examine the dynamic volatility and volatility transmission in a multivariate setting using the VAR(1)–GARCH(1,1) model for three major sectors, namely, Service, Banking and Industrial/or Insurance, in four Gulf Cooperation Council (GCC)’s economies (Kuwait, Qatar, Saudi Arabia and UAE). The second is to use the models’ results to compute and analyze the optimal weights and hedge ratios for two-sector portfolio holdings, comprised of the three sectors for each country. The results suggest that past own volatilities matter more than past shocks and there are moderate volatility spillovers between the sectors within the individual countries, with the exception of Qatar. Moreover, the values for ratios of hedging long positions with short positions in the GCC sectors are smaller than those for the US equity sectors. The optimal portfolio weights favor the Banking/financial sector for Qatar, Saudi Arabia and UAE and the Industrial sector for Kuwait.

Introduction

In developed countries, equity sector investing has been popular for many years. Investors invest in defensive stocks, such as those of the non-cyclical consumer goods sector, when the economy is teetering into recession. They invest in high tech sector's stocks when the economy is booming. In international investing, portfolio managers who follow the top down approach usually pick countries and then sectors. Even informed investors choose sectors without paying much attention to interactions and volatility transmission among sectors. In frontier markets such as the markets of the rich oil-producing countries, sector investing has not yet reached similar popularity and their markets lack organized sector indices. While there have been studies that examine the transmission of returns among individual sectors within a system, information is still needed on how volatility spillovers occur among sectors in multivariate settings. This knowledge is particularly useful because of the increase in globalization and contagion among world financial markets. The current transmission of high volatility among sectors of individual countries and among countries is a vivid and topical example.

More recent literature on Middle East and North Africa (MENA) market volatility uses univariate GARCH models and examines volatility behavior at the market index level. Hammoudeh and Li (2008) examine sudden changes in volatility for five GCC stock markets at the market index level, using the iterated cumulative sums of squares (ICSS) algorithm, and analyze their impacts on the estimated persistence of volatility. They find that most of these stock markets are more sensitive to major global events than to local and regional factors.

Zarour and Siriopoulos (2008) use the univariate CGARCH model developed by Engle and Lee (1993) to investigate the existence of volatility decomposition into short run and long run components. They apply this model to daily market index returns data for nine emerging markets in the Middle East region, including three of our GCC countries. Hammoudeh and Choi (2007) employ the univariate GARCH approach with Markov-switching to study the volatility behavior for the transitory and permanent components of the individual GCC market indices, allowing for two volatility regimes to exist. While Malik and Hammoudeh (2007) use trivariate GARCH models, their systems include one individual GCC market index, the WTI oil price and S&P 500 index to analyze return volatility transmission for three GCC markets. The volatility transmission does not involve more than one GCC market within one system.

This paper uses a more recent multivariate technique that examines shock and volatility transmission among three sectors, namely Banking, Industrial and Service, for Kuwait, Qatar and Saudi Arabia, and Financial, Insurance and Service, for UAE which does not have data for the Industrial sector.3 The technique is the vector autoregressive moving average GARCH (VAR–GARCH) model developed by Ling and McAleer (2003) (see Chan, Lim, & McAleer, 2005 for an early application of the model). This method enables us to examine the conditional volatility and conditional correlation cross effects with meaningful estimated parameters and less computational complications, as compared with other methods such as the BEKK model of Engle and Kroner (1995). BEKK is a multivariate GARCH(1,1) model with dynamic covariances and dynamic correlations, but typically is not attached to a VAR(1) model. The VAR(1) version of BEKK has not yet been analyzed theoretically (see McAleer, Chan, Hoti, & Lieberman, 2008 for further theoretical details). For more than four or five assets or commodities, BEKK typically does not converge because it has far too many parameters. In short, there is little argument in favor of BEKK, other than that it leads to a positive definite dynamic covariance matrix (see McAleer, 2005 for further elaboration).

The paper has two main objectives. (1) To examine own conditional volatility for each sector and conditional cross sector volatility transmission of the three major equity sectors in the four individual GCC stock markets, using the popular version of the vector autoregressive moving average GARCH model, which is the VAR(1)–GARCH model. This method enables an examination of the conditional volatility and conditional correlation cross effects with meaningful estimated parameters and less computational complication compared with several other methods. (2) To use the estimated results to compute the weights of the sectors in an optimal portfolio of each GCC country, and the optimal hedge ratios that minimize overall risk for holding the sectors in portfolios without affecting the expected returns in the individual country.

The empirical results for the first objective suggest that past own volatility and not past shocks is the stronger driver in determining future volatility for the GCC frontier stock markets. This implies that fundamentals matter more than news. Those results also show moderate volatility spillovers between the sectors within the individual countries, with the exception of Qatar which demonstrates strong spillovers. The results on the second objective imply that optimal portfolio weights of investors should own much more Banking stocks than Service or Industrial stocks in Saudi Arabia and Qatar, and more financial stocks than Service or Insurance stocks in UAE in order to minimize risk without lowering expected returns. Investors in Kuwait hold more Industrial stocks. The values for the hedge ratios for the GCC sectors are smaller than those for US equity sectors, reflecting the possibility of greater hedging effectiveness in GCC markets than in the USA, thereby leading to more sophisticated hedging techniques and strategies. These empirical results are important for the GCC countries which have recently embarked on establishing equity funds for both individual and institutional investors.

The remainder of the paper is organized as follows. Section 2 provides a description of the data and summary statistics. Section 3 presents the empirical VAR–GARCH model. Section 4 discusses the empirical results and Section 5 provides the economic implications for designing optimal portfolios and formulating optimal hedging strategies. Section 6 gives some concluding comments.

Section snippets

Data description

The data cover the three major sector daily indices for four of the six GCC countries, namely Saudi Arabia, Kuwait, Qatar and UAE. The primary focus of the paper is on the three most important sectors in each country. The sectors are the Service, Industrial and Banking sectors for the first three countries, and Service, Insurance and Financial for UAE, which does not have an index for the Industrial sector. Size of market capitalization and data availability on the sector indices have been

Empirical model

As indicated above, the univariate GARCH approach has been used in modeling volatility in the general indices of the GCC stock markets. Our objective is to apply recent techniques in modeling volatility to upgrade the use of the univariate GARCH approach to a multivariate system. While the BEKK model provides a multivariate GARCH(1,1) framework with dynamic covariances and dynamic correlations, this model does not have a VAR attached to it because the BEKK VAR distribution has not theoretically

Empirical results

We will discuss the empirical results in terms of own sector volatility and shock dependence, inter-sector volatility, shock spillover and political risk for the three sectors in each of the four GCC countries. As is the case with the BEKK version of the multivariate GARCH model, we are also constrained by the number of sectors that can be included in the system to achieve computational convergence.

Implications for portfolio designs and hedging strategies

We now provide two examples using the estimates of the GCC equity sector markets for portfolio design and hedging strategies.

Conclusions

This study examines own volatility, shocks and inter-shock and volatility transmissions in three equity sectors of four Gulf Cooperation Council (GCC). The sectors are Service, Banking and Industrial or insurance and the GCC countries are Kuwait, Qatar, Saudi Arabia and UAE. The study uses the popular version, the VAR–GARCH model, of the VARMA-model to achieve the results. In turn the results are used to estimate the risk-minimizing hedge ratios to assess the hedge effectiveness among the

Acknowledgements

The authors wish to thank Farooq Malik, Mark Thompson and two anonymous referees of this journal for helpful comments. We also have a special thank for Aksel Kibar and Talal Al-Jandali for providing information and insights on the equity sectors. The third author is most grateful for the financial support of the Australian Research Council.

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