Estimation of Dynamic VaR in Chinese Stock Markets Based on Time Scale and Extreme Value Theory

摘要

The accuracy and time scale invariance of value-at-risk (VaR) measurement methods for different stock indices and at different confidence levels are tested. Extreme value theory (EVT) is applied to model the extreme tail of standardized residual series of daily/weekly indices losses, and parametric and nonparametric methods are used to estimate parameters of the general Pareto distribution (GPD) , and dynamic VaR for indices of three stock markets in China. The accuracy and time scale invariance of risk measurement methods through back-testing approach are also examined. Results show that not all the indices accept time scale invariance; there are some differences in accuracy between different indices at various confidence levels. The most powerful dynamic VaR estimation methods are EVT-GJR-Hill at 97. 5% level for weekly loss to Shanghai stock market, and EVT-GARCH-MLE (Hill) at 99.0% level for weekly loss to Taiwan and Hong Kong stock markets, respectively.