Evaluation of Value-at-Risk Estimation Using Long Memory Volatility Models: Evidence from Stock Exchange of Thailand

Abstract

This paper examines the accuracy of Value-at-Risk (VaR) estimation in the Stock Exchange of Thailand. We apply standard conditional volatility models (GARCH) and the GARCH model with long memory process (FIGARCH) in calculation of VaR. The empirical results from R|S statistics show that there is significant evidence of long memory process in volatility but not in mean of SET50 index returns. Comparing accuracy of VaR estimation, the results from the Kupiec-LR test show that 1-day ahead 1% VaR values calculated using FIGARCH(1,d,1) model with normal innovations are more accurate than those generated using short memory GARCH(1,1) models. Considering the Bank of International Settlement (BIS)’s regulatory back-testing, the results also confirm that the long memory models provide better performance than those of the standard GARCH models. In summary, our empirical results indicate that long-range memory could provide better performance in risk management than that of standard GARCH in the case of Stock Exchange of Thailand. However, our results from FIGARCH still do not outperform those of the asymmetric GARCH.

Keywords: Value-at-Risk, Long Memory Process, FIGARCH, Stock Exchange of Thailand

JEL Classification: G11, G15, C32, C46