Estimation and Hedging Effectiveness of Time-Varying Hedge Ratio: Nonparametric Approaches

Many studies have estimated the optimal time-varying hedge ratio using futures, with most employing a bivariate generalized autoregressive conditional heteroscedasticity (BGARCH) model or a random coefficient model to estimate the time-varying hedge ratio. However, it has been argued that when the variability of the estimated time-varying hedge ratio is large, this ratio's hedging performance is not as good as that of the unconditional (constant) hedge ratio. This study proposes a nonparametric estimation approach to estimate and evaluate the optimal conditional hedge ratio. This method produces a time-varying hedge ratio with less volatility than those obtained from the BGARCH and random coefficient models. We evaluate the hedging performance of the various models using soybean oil, corn, S&P 500, and Hang Seng futures indices. The empirical results support the proposed nonparametric approach in terms of both in-sample and out-of-sample performance. (c) 2015 Wiley Periodicals, Inc. Jrl Fut Mark 36:968-991, 2016