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The IUP Journal of Applied Finance
Pricing and Hedging Copper Futures on the London Metal Exchange
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The purpose of this paper is to examine the behavior of copper spot prices in London Metal Exchange. Besides, we examine the relation between hedging effectiveness and the maturity of the contract. This research provides an empirical comparison of different econometric techniques in the context of hedging the market risk of copper traded on the London Metal Exchange. It is found that the VAR-MGARCH model estimates of time-varying hedge ratio provide highest variance reduction.

 
 
 

Among all the metal commodities, modeling price behavior of copper poses the biggest challenge to researchers and practitioners. In the earliest reduced form model for commodity prices (Brennan and Schwartz, 1985), the spot commodity price follows a geometric Brownian motion and the convenience yield is treated as a dividend yield. This specification is inappropriate since it does not take into account the mean reversion property of spot commodity prices and neglects the inventory-dependence property of the convenience yield. Gibson and Schwartz (1990) introduce a two-factor, constant volatility model where the spot price and the convenience yield follow a joint stochastic process with constant correlation. Brennan and Schwartz (1985), Gibson and Schwartz (1990) and Schwartz (1997) found that the one-factor models fail to capture the erratic nature of the commodity price. So, the good parametric model describing the stochastic process of commodity prices should capture the characteristics of the evolution of commodity prices.

As the commodity spot prices have been highly volatile in recent years, the volatility should be examined. The Stochastic Volatility (SV) processes are flexible enough to allow us to capture the special characteristics of copper prices. In this paper, we consider the mean reverting process of Deng (1999) and the mean reverting square root process of Heston (1993).

Rising price volatility has given rise to a number of financial instruments that allow investor to minimize risk and maximize expected return. Financial derivatives are contractual agreements with a value which changes in response to price movements in a related commodity. So, the main reason to use future contracts is to offset the risk exposures of any fluctuations in price. The combinations of cash and futures positions typically expressed in terms of proportion of cash to futures positions for an asset are referred to as optimal hedge ratios.

There are various approaches that have been developed to estimate the optimal hedge ratio. The Minimum Variance Hedge Ratio (MVHR), the Ordinary Least Squares (OLS) model, and the Vector Autoregressive (VAR) model estimate constant hedge ratio over time. Whereas, the Bollerslev, Engle, Kroner and Kraft (BEKK) model and the VAR model with bivariate generalized autoregressive conditional heteroscedasticity model, VAR-MGARCH, estimate the time-varying hedge ratios. The models estimating constant hedge ratio are based on the assumption that the joint distribution of spot and futures prices are invariant over time. Marmer (1986) examines the effectiveness of the MVHR, and shows that the utility of this approach is limited. Benninga et al. (1984) derives the MVHR from an OLS regression with the spot price changes as the dependent variable and futures price changes as the explanatory variable. The MVHR is the slope coefficient of the OLS regression. The optimal hedge ratio is the covariance of spot prices and futures prices and variance of futures prices. Using OLS regression for estimating the hedge ratio has been criticized for two reasons. Firstly, it is based on the assumption of unconditional distribution of spot and futures prices. Secondly, the OLS regression is based on the assumption that the relationship between spot and futures prices is time invariant.

 
 
 

Applied Finance Journal, Winter Blues, Stock Market Returns, Tunisian Stock Exchange, Trading Strategies, Stock Market Anomalies, Tunisian Stock Market, OLS Regression Method, Clinical Research, Risk Aversion.