Financial risk evaluations in Malaysian Stock Exchange using extreme-value theory and component-ARCH Model

Abstract

This study investigates the value-at-risk (VaR) using nonlinear time-varying volatility (ARCH model) and extreme-value-theory (EVT) methodologies. Similar VaR estimation and prediction are observes under the EVT and heavy-tailed long-memory ARCH approaches. The empirical results evidence the EVT-based VaR are more accurate but only at higher quantiles. It is also found that EVT approach is able to provide a convenient framework for asymmetric properties in both the lower and upper tails which implies that the risk and reward are not equally likely for the short- and long-trading positions in Malaysian stock market.