Robust Estimation of the Optimal Hedge Ratio

When using derivative instruments such as futures in order to hedge a portfolio of risky assets, the primary objective is to estimate the optimal hedge ratio (OHR). When agents have mean-variance utility and the futures price follows a martingale, the OHR is equivalent to the minimum variance hedge ratio, which can be estimated by regressing the spot market return on the futures market return using ordinary least squares. In order to accommodate timevarying volatility in asset returns, estimators based on rolling windows, GARCH or EWMA models are commonly employed. However, all of these approaches are based on the sample variance and covariance estimators of returns, which while consistent irrespective of the underlying distribution of the data, are not in general efficient. In particular, when the distribution of the data is leptokurtic, as is commonly found for short horizon asset returns, these estimators will attach too much weight to extreme observations. This paper proposes an alternative to the standard approach to the estimation of the OHR that is robust to the leptokurtosis of returns. We use the robust OHR to construct a dynamic hedging strategy for daily returns on the FTSE100 index using index futures. We estimate the robust OHR using both the rolling window approach and the EWMA approach, and compare our results to those based on the standard rolling window and EWMA estimators. It is shown that the robust OHR yields a hedged portfolio variance that is marginally lower than that based on the standard estimator. Moreover, the variance of the robust OHR is as much as 70% lower than the variance of the standard OHR, substantially reducing the transaction costs that are associated with dynamic hedging strategies.