Forecasting Value-at-Risk: A Comparison of Alternative Models

• Main Authors: Jong Kuo, 郭蓉
• Other Authors: Ching-Chuan Tsong
• Format: Others
• Language: zh-TW
• Published: 2008
• Online Access: http://ndltd.ncl.edu.tw/handle/ky4sqy

碩士 === 國立暨南國際大學 === 經濟學系 === 96 === This research aims at the stock market of international as substantial evidence object and adopts the GARCH, RiskMetrics, Historical Simulation (HS), Hybrid, QML GARCH and the combination of QML GARCH and EVT (EVT for short). We use this six models to estimate the maximum potential loss due to the volatility and we use the TUFF test, the unconditional coverage test and the conditional coverage test to evaluate the accuracy of VaR. The GARCH, RiskMetrics and EVT model perform well at 1-day ahead. With each stock separately, AORD, FTSE and TW can fitted by the QML GARCH and EVT model; The performance of RiskMetrics, QML GARCH or EVT model are good in the FCHI, S& P 500 and STI data; GARCH and RiskMetrics model are suggested to fit the DJI data; Parametric models and nonparametric models perform well at the HIS but we didn’t suggest to use HS and Hybrid model; Using RiskMetrics and QML GARCH model to forecast the VaR of NASDAQ can get a good result. All the data didn’t perform well under the hypothesis of coverage rate is 0.05 or 0.1 at 2-days ahead. The HS and Hybrid model are the best model but the EVT is the worst model under the hypothesis of coverage rate is 0.01. The performance of GARCH family wasn’t well which conjecture that may be with the forecast period increasing or the number of simulation isn’t enough or the number of out of sample isn’t enough. With each stock separately, the AORD and FCHI data perform well at the HS and Hybrid model; The DJI was suggested using HS and QML GARCH model under 99% confidence level; The FTSE perform well in the HS and GARCH model; The HIS, STI and S&P 500 only suggest using Hybrid model; Only the HS model can estimate accurately at the NASDAQ; The data of TW was suggested to use Hybrid and GARCH model.