| Content | Bollerslev’s (1990) constant conditional correlation (CCC) and Engle’s (2002) dynamic conditional correlation (DCC) bivariate generalized autoregressive conditional heteroskedasticity (BGARCH) models are usually used to estimate time-varying hedge ratios. In this paper, we extend the above model to more flexible ones to analyze the behavior of the optimal conditional hedge ratio based on two BGARCH models: (i) adapting more flexible bivariate density functions such as a bivariate skewed-t density function; (ii) considering asymmetric individual conditional variance equations; and (iii) incorporating asymmetry in the conditional correlation equation for the DCC based model. Hedging performance in terms of variance reduction and also value at risk and expected shortfall of the hedged portfolio are also conducted. Using daily data of the spot and futures returns of corn and soybeans we find asymmetric and flexible density specifications help increase the goodness-of-fit of the estimated models, but does not guarantee higher hedging performance. We also find that there is an inverse relationship between the variance of hedge ratios and hedging effectiveness. |
