The Relationship between Crude Oil Prices and Exchange Rates

Salles, Andre Assis de (2012): The Relationship between Crude Oil Prices and Exchange Rates. Published in: China-USA Business Review , Vol. 11, No. 5 (2012): pp. 581-590.

Abstract

Crude oil prices are influenced by several events that occur randomly, for example, the weather, the available stocks of oil, the economic growth, the variation in the industrial production, political or geopolitical aspects, exchange rate movements, and so on. Oil price volatility brings uncertainties for the world economy. Despite the difficulty in working with oil price time series, a lot of researches have been developing ways to better understand the stochastic process which represents oil prices movements. This work introduces an alternative methodology, with a Bayesian approach, for the construction of forecasting models to study the returns of oil prices. The methodology introduced here takes in consideration the violation of homoskedasticity and the occurrence of abnormal information, or the non-Gaussian distribution, in the construction of the price forecast models. Moreover, this work examines the relationship between crude oil prices and exchange rate through a cointegration test. The data used in this study consists of the daily closing exchange rate of US dollar to Euro, and oil prices of WTI, West Texas Intermediate, and Brent types, from January 2005 to March 2009. The results do not show the acceptance of cointegration hypothesis. With the presented models, it is possible to infer that the exchange rate is important to explain the oil barrel returns.

• Item Type: MPRA Paper
• Language: English
• Keywords: Crude Oil Prices; Exchange Rate; Cointegration; Forecast Models; Bayesian Inference.
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C58 - Financial Econometrics; E - Macroeconomics and Monetary Economics > E4 - Money and Interest Rates > E44 - Financial Markets and the Macroeconomy; G - Financial Economics > G1 - General Financial Markets > G15 - International Financial Markets; L - Industrial Organization > L7 - Industry Studies: Primary Products and Construction > L71 - Mining, Extraction, and Refining: Hydrocarbon Fuels; O - Economic Development, Innovation, Technological Change, and Growth > O1 - Economic Development > O13 - Agriculture ; Natural Resources ; Energy ; Environment ; Other Primary Products; Q - Agricultural and Natural Resource Economics ; Environmental and Ecological Economics > Q4 - Energy

References

[1] Akgiray, V. (1989). Conditional heteroskedasticity in time series of stock returns: Evidence and forecasts. Journal of Business, 62, 55-80.

[2] Chen, S., & Chen, H. (2007). Oil prices and real exchange rates. Energy Economics, 29, 390-404.

[3] Congdon, P. (2001). Bayesian statistical modeling. New York, N.Y.: John Wiley & Sons.

[4] Engle, R. (1982). Autoregressive conditional heteroskedasticity with estimates of United Kingdom inflations. Econometrica, 50, 987-1007.

[5] Gelfand, A., & Ghosh, S. (1998). Model Choice: A Minimum Posterior Predictive Loss Approach. Biometrika, 85(1), 1-11.

[6] Granger, C., & Swanson, N. (1977). An introduction to stochastic unit-root processes. Journal of Econometrics, 80(1), 35-62.

[7] Migon, H., & Gamerman, D. (1999). Statistical inference: An integrated approach. New York, N.Y.: Arnold Publishers.

[8] Panas, E., & Ninni, V. (2000). Are oil markets chaotic? A non-linear dynamic analysis. Energy Economics, 22, 549-568.

[9] Sadorsky, P. (2000). The empirical relationship between energy futures prices and exchange rates. Energy Economics, 22, 235-266.

[10] Salles, A. (2007). A Bayesian approach for the determination of the systematic risk in the international stock market. Proceedings of the 56th. Session of the International Statistical Institute—ISI 2007.

[11] Spiergelhalter, D., Best, N., Carlin, B., & Linde, A. (2002). Bayesian measures of model complexity and fit. Journal of the Royal Statistical Society, B(64), 583-639.

[12] Spiergelhalter, D., Thomas, A., & Best, N. (2003). User manual WinBUGS (version 1.3). MRC Biostatistics Unit, Institute of Public Health, Cambridge, UK.

[13]Wang, S., Yu, L., & Lai, K. (2005). Crude oil price forecasting with TEI@I methodology. Journal of Systems Science and Complexity, 18, 145-166.

[14] Xie, W., Yu, L., Xu, S., & Wang, S. (2006). A new method for crude oil price forecasting based on support vector machine. In V. N. Alexandrov, et al. (Eds.), ICCS 2006, Part IV, LNCS 3994, 444-454.