Abstract
This paper aims to demonstrate the superiority of Extended Mean-Gini (EMG) framework which is consistent with the second-order of stochastic dominance theory. The study provides a comprehensive analysis of investors’ distinct risk averse behavior towards optimal futures hedging strategy. The empirical distribution function method and the more efficient kernel estimation method are employed in the estimation of EMG hedge ratios. Furthermore, the moving data window procedure is used to examine the stability of the dynamic hedge ratios. The research is conducted on Malaysian Crude Palm Oil and CPO Futures markets for the period of 16th March 1995 to 28th June 2011. The empirical results show that the EMG approach is apparently more appropriate than the MV approach where EMG framework incorporates the risk aversion factor. The study also shows the instability of dynamic hedge ratios across time horizons hence not favorable to investors who adopt the “buy and hold” strategy.
Highlights
The Mean-Variance (MV) framework which is consistent with the normality (Gaussian) assumption is widely used by practitioners in futures hedging
This paper aims to demonstrate the superiority of Extended Mean-Gini (EMG) framework which is consistent with the second-order of stochastic dominance theory
Does the normality assumption hold for financial asset returns? If not, any alternative framework could potentially model the non-normally distributed asset returns? To address the problem, this study presents a stochastic dominance approach, the Extended Mean-Gini (EMG) framework, to measure the risk and hedging effectiveness in futures hedging strategies without restricted to the normality assumption
Summary
The Mean-Variance (MV) framework which is consistent with the normality (Gaussian) assumption is widely used by practitioners in futures hedging. It has been long noted by the quantitative finance community that most of the financial asset returns are non-normal. The financial turmoil of 2008 is among many such rare and unpredictable financial crises over past decades that raise public. Does the normality assumption hold for financial asset returns? Any alternative framework could potentially model the non-normally distributed asset returns? This study presents a stochastic dominance approach, the Extended Mean-Gini (EMG) framework, to measure the risk and hedging effectiveness in futures hedging strategies without restricted to the normality assumption
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