The economic value of co-movement between oil price and exchange rate using copula-based GARCH models

The US dollar is used as the primary currency of international crude oil trading; as such, the recent substantial depreciation in the US dollar has resulted in a corresponding increase in crude oil prices. In addition, oil price and exchange-rate returns have been shown to be skewed and leptokurtic, and to exhibit an asymmetric or tail dependence structure. Therefore, this study proposes dynamic copula-based GARCH models to explore the dependence structure between the oil price and the US dollar exchange rate. More importantly, an asset-allocation strategy is implemented to evaluate economic value and confirm the efficiency of the copula-based GARCH models. In terms of out-of-sample forecasting performance, a dynamic strategy based on the CGARCH model with the Student-t copula exhibits greater economic benefits than static and other dynamic strategies. In addition, the positive feedback trading activities are statistically significant within the oil market, but this information does not enhance the economic benefits from the perspective of an asset-allocation decision. Finally, a more risk-averse investor generates a higher fee for switching from a static strategy to a dynamic strategy based on copula-based GARCH models.

[1]  H. Joe Asymptotic efficiency of the two-stage estimation method for copula-based models , 2005 .

[2]  A. Mollick,et al.  Oil price fluctuations and U.S. dollar exchange rates , 2010 .

[3]  Enrique Sentana,et al.  Feedback Traders and Stock Return Autocorrelations: Evidence from a Century of Daily Data , 1992 .

[4]  R. Nelsen An Introduction to Copulas , 1998 .

[5]  A Simultaneous Equations Model for World Crude Oil and Natural Gas Markets , 2005 .

[6]  Yi-Ming Wei,et al.  Estimating ‘Value at Risk’ of crude oil price and its spillover effect using the GED-GARCH approach , 2008 .

[7]  Chris Kirby,et al.  The economic value of volatility timing using “realized” volatility ☆ , 2003 .

[8]  Cees Diks,et al.  The relationship between crude oil spot and futures prices: cointegration, linear and nonlinear causality , 2008 .

[9]  H. Joe Multivariate models and dependence concepts , 1998 .

[10]  Michael McAleer,et al.  Crude Oil Hedging Strategies Using Dynamic Multivariate GARCH , 2010 .

[11]  Söhnke M. Bartram,et al.  The Euro and European Financial Market Dependence , 2007 .

[12]  Ramazan Sarı,et al.  Dynamics of oil price, precious metal prices, and exchange rate , 2010 .

[13]  L. Glosten,et al.  On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks , 1993 .

[14]  B. Hansen Autoregressive Conditional Density Estimation , 1994 .

[15]  Tony S. Wirjanto,et al.  The empirical role of the exchange rate on the crude-oil price formation , 2004 .

[16]  Stephen L Taylor,et al.  The Euro and European Financial Market Integration , 2004 .

[17]  Q. Akram,et al.  Oil Prices and Exchange Rates: Norwegian Evidence , 2004 .

[18]  Chris Kirby,et al.  The Economic Value of Volatility Timing , 2000 .

[19]  G. Cifarelli,et al.  Oil Price Dynamics and Speculation: A Multivariate Financial Approach , 2008 .

[20]  Yufeng Han,et al.  Asset Allocation with a High Dimensional Latent Factor Stochastic Volatility Model , 2005 .

[21]  Chris Kirby,et al.  The Economic Value of Volatility Timing Using 'Realized' Volatility , 2001 .

[22]  Minxian Yang,et al.  Asymmetric Volatility in the Foreign Exchange Markets , 2006 .

[23]  Matteo Manera,et al.  Modelling Dynamic Conditional Correlations in Wti Oil Forward and Futures Returns , 2004 .

[24]  Pierre Giot,et al.  Market risk in commodity markets: a VaR approach , 2003 .

[25]  G. Cifarelli,et al.  Oil price dynamics and speculation , 2010 .

[26]  Andrew J. Patton Modelling Asymmetric Exchange Rate Dependence , 2006 .

[27]  E. Luciano,et al.  Copula methods in finance , 2004 .