Does long memory matter in forecasting oil price volatility?
This study attempts to introduce an appropri¬¬ate model for modeling and forecasting Iran’s crude oil price volatility. Therefore, this hypothesis will be tested about whether long memory feature matters in forecasting the price of this commodity. For this purpose, using the Iran’s weekly crude oil price data, the long memory feature will be considered in the return and volatilities series, and the fractal markets hypothesis will also be examined about Iran’s oil market. In addition, from among the different conditional heteroscedasticity models, the best model for forecasting oil price volatilities will be selected based the forecasting error criterion. The main hypothesis of the study will be tested out using Clark-West test (2006). The results of our study confirmed the existence of long memory feature in both mean and variance equations of these series. But from among the conditional heteroscedasticity models, the ARFIMA-FIGARCH model was selected as the best model based on the Akaike and Schwarz information criteria (for modeling), and also the MSE criterion (for forecasting). Finally, the Clark-West test showed that the long memory feature is important in forecasting oil price volatilities.
|Date of creation:||19 Apr 2013|
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- Mehrara, Mohsen & Oskoui, Kamran Niki, 2007. "The sources of macroeconomic fluctuations in oil exporting countries: A comparative study," Economic Modelling, Elsevier, vol. 24(3), pages 365-379, May.
- Kang, Sang Hoon & Kang, Sang-Mok & Yoon, Seong-Min, 2009. "Forecasting volatility of crude oil markets," Energy Economics, Elsevier, vol. 31(1), pages 119-125, January.
- Todd E. Clark & Kenneth D. West, 2004.
"Using out-of-sample mean squared prediction errors to test the Martingale difference hypothesis,"
Research Working Paper
RWP 04-03, Federal Reserve Bank of Kansas City.
- Clark, Todd E. & West, Kenneth D., 2006. "Using out-of-sample mean squared prediction errors to test the martingale difference hypothesis," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 155-186.
- Kang, Sang Hoon & Yoon, Seong-Min, 2013.
"Modeling and forecasting the volatility of petroleum futures prices,"
Elsevier, vol. 36(C), pages 354-362.
- Seong-Min Yoon & Sang Hoon Kang, 2012. "Modelling and forecasting the volatility of petroleum futures prices," EcoMod2012 3944, EcoMod.
- Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
- Kyongwook Choi & Shawkat Hammoudeh, 2009. "Long Memory in Oil and Refined Products Markets," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 97-116.
- Andrew W. Lo, A. Craig MacKinlay, 1988.
"Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test,"
Review of Financial Studies,
Society for Financial Studies, vol. 1(1), pages 41-66.
- Andrew W. Lo & A. Craig MacKinlay, 1987. "Stock Market Prices Do Not Follow Random Walks: Evidence From a Simple Specification Test," NBER Working Papers 2168, National Bureau of Economic Research, Inc.
- Tom Doan, . "VRATIO: RATS procedure to implement variance ratio unit root test procedure," Statistical Software Components RTS00231, Boston College Department of Economics.
- Aloui, Chaker & Mabrouk, Samir, 2010. "Value-at-risk estimations of energy commodities via long-memory, asymmetry and fat-tailed GARCH models," Energy Policy, Elsevier, vol. 38(5), pages 2326-2339, May.
- Farzanegan, Mohammad Reza & Markwardt, Gunther, 2008.
"The effects of oil price shocks on the Iranian economy,"
Dresden Discussion Paper Series in Economics
15/08, Dresden University of Technology, Faculty of Business and Economics, Department of Economics.
- Farzanegan, Mohammad Reza & Markwardt, Gunther, 2009. "The effects of oil price shocks on the Iranian economy," Energy Economics, Elsevier, vol. 31(1), pages 134-151, January.
- Mohammad Reza FARZANEGAN & Gunther MARKWARDT, . "The Effects of Oil Price Shocks on the Iranian Economy," EcoMod2008 23800037, EcoMod.
- Komijani, Akbar & Gandali Alikhani, Nadiya & Naderi, Esmaeil, 2012.
"The Long-run and Short-run Effects of Crude Oil Price on Methanol Market in Iran,"
45975, University Library of Munich, Germany.
- Akbar Komijani & Nadiya Gandali Alikhani & Esmaeil Naderi, 2013. "The Long-run and Short-run Effects of Crude Oil Price on Methanol Market in Iran," International Journal of Energy Economics and Policy, Econjournals, vol. 3(1), pages 43-50.
- Wei, Yu & Wang, Yudong & Huang, Dengshi, 2010. "Forecasting crude oil market volatility: Further evidence using GARCH-class models," Energy Economics, Elsevier, vol. 32(6), pages 1477-1484, November.
- Cheong, Chin Wen, 2009. "Modeling and forecasting crude oil markets using ARCH-type models," Energy Policy, Elsevier, vol. 37(6), pages 2346-2355, June.
- Engle, Robert F & Lilien, David M & Robins, Russell P, 1987. "Estimating Time Varying Risk Premia in the Term Structure: The Arch-M Model," Econometrica, Econometric Society, vol. 55(2), pages 391-407, March.
- Nese Erbil, 2011. "Is Fiscal Policy Procyclical in Developing Oil-Producing Countries?," IMF Working Papers 11/171, International Monetary Fund.
- Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996.
"Fractionally integrated generalized autoregressive conditional heteroskedasticity,"
Journal of Econometrics,
Elsevier, vol. 74(1), pages 3-30, September.
- Tom Doan, . "RATS programs to replicate Baillie, Bollerslev, Mikkelson FIGARCH results," Statistical Software Components RTZ00009, Boston College Department of Economics.
- Tim Bollerslev, 1986.
"Generalized autoregressive conditional heteroskedasticity,"
EERI Research Paper Series
EERI RP 1986/01, Economics and Econometrics Research Institute (EERI), Brussels.
- Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
- Belkhouja, Mustapha & Boutahary, Mohamed, 2011. "Modeling volatility with time-varying FIGARCH models," Economic Modelling, Elsevier, vol. 28(3), pages 1106-1116, May.
- Wei, Yu, 2012. "Forecasting volatility of fuel oil futures in China: GARCH-type, SV or realized volatility models?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(22), pages 5546-5556.
- Jian Zhou & Zhixin Kang, 2011. "A Comparison of Alternative Forecast Models of REIT Volatility," The Journal of Real Estate Finance and Economics, Springer, vol. 42(3), pages 275-294, April.
- Kittiakarasakun, Jullavut & Tse, Yiuman, 2011. "Modeling the fat tails in Asian stock markets," International Review of Economics & Finance, Elsevier, vol. 20(3), pages 430-440, June.
- Vo, Minh, 2011. "Oil and stock market volatility: A multivariate stochastic volatility perspective," Energy Economics, Elsevier, vol. 33(5), pages 956-965, September.
- Kang, Sang Hoon & Cheong, Chongcheul & Yoon, Seong-Min, 2011. "Structural changes and volatility transmission in crude oil markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(23), pages 4317-4324.
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