THE VAR EVALUATION OF SHARIAH STOCK MARKET IN MALAYSIA DURING COVID-19 PANDEMIC BY USING CONDITIONAL EVT METHOD

Abstract

In the current financial market, the Islamic stock market faced with a significant challenge to sustain and maintain its stability in intensified market volatility and unexpected extreme events. It can reduce the intensity and occurrence of financial crises by eliminating the primary vulnerabilities of the conventional system. This paper aims to identify the most effective method in risk evaluation by presenting the risk evaluation performance between conventional and Islamic stock market that focusing on extreme events in stock market returns. The data analysis is divided into two periods: normal and crisis COVID-19 periods. The empirical analysis, conducted within the sample employs the conditional extreme value theory (EVT) method that combine the filtered series of GARCH and EGARCH models. This filtered series will be used to generate the threshold by using the peak-over-threshold (POT) method. This threshold then will be used to estimate the generalized Pareto distribution (GPD) distribution to forecast the one-day ahead value-at-risk (VaR). The findings indicate that, in Shariah stock markets, the conditional EVT model demonstrates superior performance in forecasting stock market risk compared to the standard GARCH and EGARCH models.