On the Discrimination of Competing GARCH-type Models for Taiwan Stock Index Returns
Journal
經濟論文
Journal Volume
31
Journal Issue
3
Pages
369-405
Date Issued
2003
Author(s)
Abstract
In this paper, we concentrate on the discrimination between competing GARCH type models and innovation distribution assumptions for the Taiwan stock index returns. By using a two-step detection procedure, we find that the EGARCH with the ARCH-in-mean effect and the generalized error distributed innovations is the most promising one of a set of representative models. The in-sample comparison shows that the selected model generates different interpretations on the volatility from the GARCH model (with the normally distributed standardized innovations) when the market is volatile. The out-of-ample comparison demonstrates that the selected model outperforms the GARCH and RiskMetrics models for predicting the tails of the return distribution. This study also provides some empirical evidence with important implications on GARCH modeling. First, as shown by Chen and Kuan (2002) for the U.S. stock index returns, we find that the Ljung-Box, McLeod-Li, and BDS tests are unable to discover a misspecified GARCH model with the neglected asymmetric volatility effect for the Taiwan stock index return. Second, if the misspecification of the GARCH model is overlooked, then the neglected asymmetric volatility effect may be confused with the asymmetry of innovation distribution.
SDGs
Type
journal article