The tail behavior of extreme stock returns in the Gulf emerging markets: An implication for financial risk management
Purpose – In this paper, the aim is to investigate the tail behavior of daily stock returns for three emerging stock in the Gulf region (Bahrain, Oman, and Saudi Arabia) over the period 1998-2005. In addition, the aim is also to test whether the distributions are similar across these markets. Design/methodology/approach – Following McNeil and Frey, Wanger and Marsh, and Bystrom, extreme value theory (EVT) methods are utilized to examine the asymptotic distribution of the tail for daily returns in the Gulf region. As a first step and to obtain independent and identically distributed residuals series, the returns are prefiltered with an ordinary time-series model, taking into account the observed Gulf return dynamics. Then, the “Peaks-Over-Threshold” (POT) model is applied to estimate the tails of the innovational distribution. Findings – Not only is the heavy tail found to be a facial appearance in these markets, but also POT method of modelling extreme tail quantiles is more accurate than conventional methodologies (historical simulation and normal distribution models) in estimating the tail behavior of the Gulf markets returns. Across all return series, it is found that left and right tails behave very different across countries. Research limitations/implications – The results show that risk models that are able to exploit tail behavior could lead to more accurate risk estimates. Thus, participants in the Gulf equity markets can rely on EVT-based risk model when assessing their risks. Originality/value – The paper extends previous studies in two aspects. First, it extends the classical unconditional extreme value approach by first filtering the data by using AR-FIAPARCH model to capture some of the dependencies in the stock returns, and thereafter applying ordinary extreme value techniques. Second, it provides a broad analysis of return dynamics of the Gulf markets.
Volume (Year): 25 (2008)
Issue (Month): 1 (March)
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