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Interval Estimation of Value-at-Risk Based on GARCH Models with Heavy Tailed Innovations
| Content Provider | CiteSeerX |
|---|---|
| Author | Chana, Ngai Hang Dengb, Shi-Jie Liang Pengc, B. Xia, Zhendong |
| Abstract | ARCH and GARCH models are widely used to model financial market volatilities in risk management applications. Considering a GARCH model with heavy-tailed in-novations, we characterize the limiting distribution of an estimator of the conditional Value-at-Risk (VaR), which corresponds to the extremal quantile of the conditional dis-tribution of the GARCH process. We propose two methods, the normal approximation method and the data tilting method, for constructing confidence intervals for the condi-tional VaR estimator and assess their accuracies by simulation studies. Finally, we apply the proposed approach to an energy market data set. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Garch Model Interval Estimation Extremal Quantile Normal Approximation Method Garch Process Confidence Interval Energy Market Data Set Conditional Value-at-risk Risk Management Application Condi-tional Var Estimator Conditional Dis-tribution Heavy-tailed In-novations Simulation Study Financial Market Volatility |
| Content Type | Text |
