Volatility Forecasts of Alternative Bivariate GARCH Models: Evidence from the Stock Markets in Asia

雙變量GARCH模型之波動性預測：以亞洲股票市場為例

10.6545/JFS.2017.25(4).3

蘇榮斌(Jung-Bin Su)

Constant conditional correlation ； parameter estimate method ； stock market ； Bivariate GARCH ； volatility forecast ； 常數條件相關係數 ； 參數估計方法 ； 股票市場 ； 雙變量GARCH ； 波動性預測

財務金融學刊

25卷4期（2017 / 12 / 31）

83 - 123

英文

This study uses 12 bivariate generalized autoregressive conditional heteroskedasticity (GARCH) models to forecast the volatility of stock-based portfolios in Asia, and then evaluates the forecast performance for the above models. Empirical results show that, the Student’s t does not own better forecast performance, the standard and nonstandard approaches have the same forecast performance, and the constant conditional correlation (CCC) has the best forecast performance among three covariance specifications. Regarding the six GARCH models composed of three covariance specifications and two parameter estimate methods the CCC specification with the standard approach has the best forecast performance irrespective of return distribution setting.

本研究以亞洲股票市場投資組合日報酬率資料評估12個雙變量GARCH模型之波動性預測性能。由實證結果顯示Student's t分配未具較佳預測性能、標準法及非標準法具相同預測性能，而常數條件相關係數為三種共變異數中具最佳預測性能。此外，無論報酬率分配為何，有常數條件相關係數之標準模型具最佳預測性能。

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