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Volatility transmission between gold and oil futures under structural breaks. (2013)

by on September 13, 2013

This paper studies the volatility of gold and oil markets as well as the transmission of volatility between the two. They argue that unless we account for structural breaks in the volatility of gold the findings will not give us a fully accurate answer.

They firstly find that gold’s volatility went through 9 different phases over the sample used. Without breaks they found a very high degree of persistence in gold’s volatility with shocks having a half-life of 69 days. Once we allow breaks to occur this drops off significantly to 5 days. In looking at volatility transmission between the two markets they find that there are significant direct effects. This is explained as an through investor cross hedging.

They relate these results to real world investment decisions. The optimal portfolio allocation between the two assets without structural breaks is 85% in gold, 15% in oil. When we allow for structural breaks in the variance this percentage increases to 91% gold. An investor who wishes to maximize their return per unit of risk would buy more gold that in a simple model with no breaks. Their model is also shown to improve hedging decisions for investors.

Method: Iterated cumulative sum of squares, Univariate and Bivariate GARCH models.

Data: Daily COMEX gold futures and NYMEX oil futures from July 1993 to June 2010 using the nearest expiration contract.

Full Citation: Ewing, B., Malik, F. (2013) Volatility transmission between gold and oil futures under structural breaks. International Review of Economics and Finance 25 (2013) 113–121

Abstract: This paper employs univariate and bivariate GARCH models to examine the volatility of gold and oil futures incorporating structural breaks using daily returns from July 1, 1993 to June 30, 2010. We find strong evidence of significant transmission of volatility between gold and oil returns when

structural breaks in variance are accounted for in the model. We compute optimal portfolio weights and dynamic risk minimizing hedge ratios to highlight the significance of our empirical results. Our findings support the idea of cross-market hedging and sharing of common information by financial market participants.

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From → Empirical, Gold

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