Value-at-Risk Analysis for Taiwan Stock Index Futures: Fat Tails and Conditional Asymmetries in Return Innovations

摘要

This paper examines the forecasting performance of three value-at-risk (VaR) models (RiskMetrics, Normal APARCH and Student APARCH). We explore and compare two different possible sources of performance improvements: asymmetry in the conditional variance and fat-tailed distributions. Performance is assessed using a range of measures that address the accuracy and efficiency of each model. The TAIFEX and SGX-DT Taiwan stock index futures are studied using daily data. Our results suggest that for asset returns which exhibit fatter tails and volatility clustering, like the TAIFEX and SGX-DT futures, the VaR values produced by the Normal APARCH model are preferred at lower confidence levels. However, at high confidence levels, the VaR forecasts obtained by the Student APARCH model are more accurate than those generated using either the RiskMetrics or Normal APARCH models.