The Out-of-Sample Forecasting of Hedged Portfolio Variances using Bivariate Mixed Normal GARCH Models
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In this study we compare two standard bivariate GARCH models with new bivariate mixed normal GARCH models in terms of the percentage variance reduction of the out-of-sample hedged portfolio and the statistical significance test of the performance improvements using Hansen's (2001) Superior Predictive Ability statistics. All competing models are applied to estimate time-varying hedge ratios for corn and wheat, traded on the Chicago Board of Trade. The empirical results demonstrate that the standard BEKK-GARCH model significantly outperforms the other competing GARCH models at shorter horizons. However, as the hedge horizon is extended to longer than 10 days, it is evident that the mixed normal BEKK-GARCH model is the best at the usual significance level of 5%.
Abstract
In this study we compare two standard bivariate GARCH models with new bivariate mixed normal GARCH models in terms of the percentage variance reduction of the out-of-sample hedged portfolio and the statistical significance test of the performance improvements using Hansen's (2001) Superior Predictive Ability statistics. All competing models are applied to estimate time-varying hedge ratios for corn and wheat, traded on the Chicago Board of Trade. The empirical results demonstrate that the standard BEKK-GARCH model significantly outperforms the other competing GARCH models at shorter horizons. However, as the hedge horizon is extended to longer than 10 days, it is evident that the mixed normal BEKK-GARCH model is the best at the usual significance level of 5%.
참고문헌 (32)
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