Optimal futures hedging by using realized semicovariances: The information contained in signed high‐frequency returns. Issue 5 (13th March 2023) - Lai, Yu‐Sheng
Abstract: This paper proposes a realized semicovariance‐based generalized autoregressive conditional heteroskedasticity (GARCH) model for optimal futures hedging in which realized semicovariances are computed from signed high‐frequency returns. The model enables flexible, continuous leverage for equity indices and exhibits stronger responses to jointly negative return shocks than do traditional threshold‐based asymmetric GARCH models. Our results indicate that the proposed model outperforms simpler models in model fit, covariance prediction, and portfolio variance reduction and can help achieve pronounced economic gains for hedgers. The findings demonstrate that signed high‐frequency returns contain valuable information for explaining covariance asymmetries and provide managerial implications for market participants to improve risk management.