Assessing the risk forecasts for Japanese stock market

Abstract

We evaluate predictive performance of a selection of value-at-risk (VaR) models for Japanese stock market data. We consider traditional VaR models such as Riskmetrics method, historical simulation, variance–covariance method, Monte Carlo method, and their variants which are integrated with various ARCH models. Also considered are more recent models based on non-parametric quantile regression and extreme value theory (EVT). We apply these methods to the Japanese stock market index (1984–2000) and compare their performances in terms of various evaluation criteria using the method of White [Econometrica 68 (5) (2000) 1097–1126] for three out-of-sample periods of 1995–1996, 1997–1998, and 1999–2000.