Component structure for nonstationary time series: Application to benchmark oil prices
AbstractThe oil market is characterized by several hundreds of different grades of crude extracted from various locations on the planet, but prices of those grades are structured with reference to only a handful of benchmark varieties. In this context, the ability to predict near term benchmark oil prices takes on special importance. In this paper, we explore an approach to model the benchmark oil price behaviors using a structure of permanent and transitory components. This initial attempt seems very encouraging at least with respect to one-week ahead forecast and deserves further investigation. In contrast to the equities, the weekly oil permanent components do not seem to be explainable by fundamental factors. However, the returns of the short-run, transitory oil components or cycles, which differ in terms of their degrees of persistence, are mostly affected by contagion spillovers and not by the fundamentals. Their volatilities vary slightly in terms of their sensitivity to major geopolitical events. The overall findings underscore the importance of benefiting more from spillover-catching strategies over diversification ones in the short-run.
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Bibliographic InfoArticle provided by Elsevier in its journal International Review of Financial Analysis.
Volume (Year): 17 (2008)
Issue (Month): 5 (December)
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Web page: http://www.elsevier.com/locate/inca/620166
Permanent component Transitory component Kalman filter One-step ahead forecasts;
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- Chang-Jin Kim & Jeremy M. Piger, 2001.
"Common stochastic trends, common cycles, and asymmetry in economic fluctuations,"
2001-014, Federal Reserve Bank of St. Louis.
- Kim, Chang-Jin & Piger, Jeremy, 2002. "Common stochastic trends, common cycles, and asymmetry in economic fluctuations," Journal of Monetary Economics, Elsevier, vol. 49(6), pages 1189-1211, September.
- Chang-Jin Kim & Jeremy Piger, 2000. "Common Stochastic Trends, Common Cycles, and Asymmetry in Economic Fluctuations," Discussion Papers in Economics at the University of Washington 0021, Department of Economics at the University of Washington.
- Chang-Jin Kim & Jeremy Piger, 2000. "Common Stochastic Trends, Common Cycles, and Asymmetry in Economic Fluctuations," Econometric Society World Congress 2000 Contributed Papers 1465, Econometric Society.
- Chang-Jin Kim & Jeremy Piger, 2000. "Common Stochastic Trends, Common Cycles, and Asymmetry in Economic Fluctuations," Working Papers 0021, University of Washington, Department of Economics.
- Chang-Jin Kim & Jeremy Piger, 2000. "Common stochastic trends, common cycles, and asymmetry in economic fluctuations," International Finance Discussion Papers 681, Board of Governors of the Federal Reserve System (U.S.).
- Hillard G. Huntington, 1994. "Oil Price Forecasting in the 1980s: What Went Wrong?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 1-22.
- Ming Chien Lo & Jeremy Piger, 2003.
"Is the response of output to monetary policy asymmetric? evidence from a regime-switching coefficients model,"
2001-022, Federal Reserve Bank of St. Louis.
- Lo, Ming Chien & Piger, Jeremy, 2005. "Is the Response of Output to Monetary Policy Asymmetric? Evidence from a Regime-Switching Coefficients Model," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 37(5), pages 865-86, October.
- Lien, Donald & Wilson, Bradley K., 2001. "Multiperiod hedging in the presence of stochastic volatility," International Review of Financial Analysis, Elsevier, vol. 10(4), pages 395-406.
- Eduardo Schwartz & James E. Smith, 2000. "Short-Term Variations and Long-Term Dynamics in Commodity Prices," Management Science, INFORMS, vol. 46(7), pages 893-911, July.
- Hammoudeh, Shawkat & Li, Huimin, 2008. "Sudden changes in volatility in emerging markets: The case of Gulf Arab stock markets," International Review of Financial Analysis, Elsevier, vol. 17(1), pages 47-63.
- Andrew C. Harvey, 1990. "The Econometric Analysis of Time Series, 2nd Edition," MIT Press Books, The MIT Press, edition 2, volume 1, number 026208189x, June.
- Shirvani, Hassan & Wilbratte, Barry, 2007. "The permanent-transitory decomposition of the stock markets of the G7 countries: A multivariate approach," The Quarterly Review of Economics and Finance, Elsevier, vol. 47(2), pages 352-365, May.
- Bali, Turan G. & Weinbaum, David, 2007. "A conditional extreme value volatility estimator based on high-frequency returns," Journal of Economic Dynamics and Control, Elsevier, vol. 31(2), pages 361-397, February.
- Hammoudeh, Shawkat & Li, Huimin & Jeon, Bang, 2003. "Causality and volatility spillovers among petroleum prices of WTI, gasoline and heating oil in different locations," The North American Journal of Economics and Finance, Elsevier, vol. 14(1), pages 89-114, March.
- Tansuchat, R. & Chang, C-L. & McAleer, M.J., 2010.
"Crude Oil Hedging Strategies Using Dynamic Multivariate GARCH,"
Econometric Institute Report
EI 2010-10, Erasmus University Rotterdam, Econometric Institute.
- Chang, Chia-Lin & McAleer, Michael & Tansuchat, Roengchai, 2011. "Crude oil hedging strategies using dynamic multivariate GARCH," Energy Economics, Elsevier, vol. 33(5), pages 912-923, September.
- Chia-Lin Chang & Michael McAleer & Roengchai Tansuchat, 2010. "Crude Oil Hedging Strategies Using Dynamic Multivariate GARCH," KIER Working Papers 743, Kyoto University, Institute of Economic Research.
- Roengchai Tansuchat & Chia-Lin Chang & Michael McAleer, 2010. "Crude Oil Hedging Strategies Using Dynamic Multivariate GARCH," CIRJE F-Series CIRJE-F-704, CIRJE, Faculty of Economics, University of Tokyo.
- Roengchai Tansuchat & Chia-Lin Chang & Michael McAleer, 2010. "Crude Oil Hedging Strategies Using Dynamic Multivariate GARCH," Working Papers in Economics 10/03, University of Canterbury, Department of Economics and Finance.
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