Asymmetric GARCH Value at Risk of DIA

• Main Authors: Shih-Ting Chou, 周詩婷
• Other Authors: 蘇永成
• Format: Others
• Language: en_US
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/67545150900818327581

碩士 === 國立臺灣大學 === 財務金融學研究所 === 96 === In this study we adopt two asymmetric GARCH models, with GJR-GARCH represent the rotation asymmetric effect; and NA-GARCH for the shift asymmetric effect, to compare their performance on VaR forecasting to the symmetric GARCH model. With variance of their mean equations which are ARMA(1,1), AR(1), MA(1), and “in-mean”. We introduce the close price of DIA and use its daily return as sample, stretching from Dec. 31, 2000 to Dec. 29, 2006. We use 860 observations for parameters estimates; the rest 400 will be used by different models to forecast VaR in 99% and 95% confidence level then been evaluated. The major findings in this study are: 1. The asymmetric GJR-GARCH and NA-GARCH models have fairly well performance on VaR forecasting, while symmetric GARCH model has the poorest performance. In 99% confidence level, MA(1)-GARCHM(1,1) generate fourteen violations and is the one model to exceed Basel Non-Rejection Range. This thesis conclude that in forecasting VaR of DIA, asymmetric GARCH models have superior performance than that of the symmetric one, and the GJR-GARCH with rotation effect has the most outstanding outcome. 2. In asymmetric GARCH models, GJR families that represent rotation asymmetric and NA families that represent shift, we can’t find solid proof of whether introducing new information would always improve the forecast accuracy of a model, for the four mean equations generate almost identical violation rate under the same asymmetric GARCH model. In the mean time, we can’t find significant improvement of VaR forecasting ability of the model with more parameters. Also the ARMA(1,1) mean equation doesn’t generate obvious more violations than other models do, so we can’t determine any clearly over-fitting situation.