Analyzing commodity futures and stock market indices: Hedging strategies using asymmetric dynamic conditional correlation models

Abstract

The focus of this paper is on analyzing the behavior of commodity futures indices and stock market indices in terms of price volatility and hedging. Specifically, we explore the weekly hedging strategies generated by two types of asymmetric dynamic conditional correlation (DCC) processes: return-based and range-based. We evaluate the effectiveness of these strategies for both short and long hedgers, using measures such as semi-variance, low partial moment, and conditional value-at-risk. Our findings reveal that, in general, the range-based DCC model is more effective than the return-based DCC model for hedging purposes.