Long range dependence in an emerging stock market’s sectors: volatility modelling and VaR forecasting

This study evaluates the sector risk of the Qatar Stock Exchange (QSE), a recently upgraded emerging stock market, using value-at-risk models for the 7 January 2007–18 October 2015 period. After providing evidence for true long memory in volatility using the log-likelihood profile test of Qu and splitting the sample and dth differentiation tests of Shimotsu, we compare the FIGARCH, HYGARCH and FIAPARCH models under normal, Student-t and skewed-t innovation distributions based on in and out-of-sample VaR forecasts. The empirical results show that the skewed Student-t FIGARCH model generates the most accurate prediction of one-day-VaR forecasts. The policy implications for portfolio managers are also discussed.     

Are Investors Concerned with Stock Market Upgrades? Evidence from Multivariate Framework Analysis

This study aims to examine the return and volatility responses to the announcement of stock market upgrades. It measures the direct effects of the recent Morgan Stanley Capital International (MSCI) upgrade of the Qatar, Dubai, and Abu Dhabi stock exchanges from frontier to emerging markets by applying a nontraditional dummy variable event study using multivariate BEKK and DCC GARCH models. The results show clear evidence that contradicts the free information hypothesis and supports the price pressure hypothesis. Initially, the MSCI upgrade led to positive feedback from active investors due to the belief that this announcement will attract foreign institutional investors who play a vital role in improving the market’s performance.