| Summary: | 碩士 === 國立臺北大學 === 國際財務金融碩士在職專班 === 100 === This study selects the appropriate model to match volatility of Russia stock market from ARCH, GARCH and EGARCH models and find the appropriate distribution assumption from normal, t and GED distribution. In the meantime, I use “5 days rolling return” to be the proxy of true volatility. This study uses three kinds of loss functions, including RMSE, MAE and THEIL.
The empirical result indicates that the GARCH-GED model, EGARCH-GED model and GARCH- ND model have superior forecasting ability of volatility for Russia stock market with RMSE loss functions. However, the GARCH-GED model has the best performance when using MAE as the loss function. As for THEIL, the EGARCH-t and GARCH-t are top 2 models, the former better than the latter.
On the basis of the empirical result, there are high performance to forecaste volatility of Russia stock market which is asymmetric when asymmetric models and correct distribution assumption be used but distribution assumption seems more crucial than alternative asymmetric for volatility forecasting.
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