A Skewed Student-T Value-at-Risk Approach for Long Memory Volatility Processes in Japanese Financial Markets

This paper investigates the relevance of skewed Student-t distributions in capturing long memory volatility properties in the daily return series of Japanese financial data (Nikkei 225 Index and JPY-USD exchange rate). For this purpose, we assess the performance of two long memory Value-at-Risk (VaR) models (FIGARCH and FIAPARCH VaR model) with three different distribution innovations: the normal, Student-t, and skewed Student-t distributions. From our results, we find that the skewed Student-t distribution model produces more accurate VaR estimations than normal and Student-t distribution models. Thus, accounting for skewness and excess kurtosis in the asset return distribution can provide suitable criteria for VaR model selection in the context of long memory volatility and enhance the performance of risk management in Japanese financial markets