Oil Price Forecasting Using a Time-Varying Approach
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- Alquist, Ron & Kilian, Lutz & Vigfusson, Robert J., 2013.
"Forecasting the Price of Oil,"
Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 427-507,
Elsevier.
- Ron Alquist & Lutz Kilian & Robert Vigfusson, 2011. "Forecasting the Price of Oil," Staff Working Papers 11-15, Bank of Canada.
- Ron Alquist & Lutz Kilian & Robert J. Vigfusson, 2011. "Forecasting the price of oil," International Finance Discussion Papers 1022, Board of Governors of the Federal Reserve System (U.S.).
- Kilian, Lutz & Alquist, Ron & Vigfusson, Robert J., 2011. "Forecasting the Price of Oil," CEPR Discussion Papers 8388, C.E.P.R. Discussion Papers.
- Huang, Paoyu & Ni, Yensen, 2017. "Board structure and stock price informativeness in terms of moving average rules," The Quarterly Review of Economics and Finance, Elsevier, vol. 63(C), pages 161-169.
- Zhang, Yue-Jun & Zhang, Lu, 2015. "Interpreting the crude oil price movements: Evidence from the Markov regime switching model," Applied Energy, Elsevier, vol. 143(C), pages 96-109.
- Chiarella, Carl & He, Xue-Zhong & Hommes, Cars, 2006.
"A dynamic analysis of moving average rules,"
Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1729-1753.
- Chiarella, C. & He, X.-Z. & Hommes, C.H., 2004. "A Dynamic Analysis of Moving Average Rules," CeNDEF Working Papers 04-14, Universiteit van Amsterdam, Center for Nonlinear Dynamics in Economics and Finance.
- Carl Chiarella & Xue-Zhong He & Cars Hommes, 2004. "A Dynamic Analysis of Moving Average Rules," Research Paper Series 133, Quantitative Finance Research Centre, University of Technology, Sydney.
- Cars Hommes & Carl Chiarella & Xue-Zhong He, 2004. "A Dynamical Analysis of Moving Average Rules," Computing in Economics and Finance 2004 238, Society for Computational Economics.
- Carl Chiarella & Tony He & Cars H. Hommes, 2005. "A Dynamic Analysis of Moving Average Rules," Tinbergen Institute Discussion Papers 05-057/1, Tinbergen Institute.
- Pesaran, M Hashem & Timmermann, Allan, 1992.
"A Simple Nonparametric Test of Predictive Performance,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 10(4), pages 561-565, October.
- Pesaran, M.H. & Timmermann, A., 1990. "A Simple, Non-Parametric Test Of Predictive Performance," Cambridge Working Papers in Economics 9021, Faculty of Economics, University of Cambridge.
- Pesaran, M.H. & Timmermann, A., 1990. "A Simple Non-Parametric Test Of Predictive Performance," Papers 29, California Los Angeles - Applied Econometrics.
- Ghoshray, Atanu & Johnson, Ben, 2010. "Trends in world energy prices," Energy Economics, Elsevier, vol. 32(5), pages 1147-1156, September.
- Zhang, Mengqi & Jiang, Xin & Fang, Zehua & Zeng, Yue & Xu, Ke, 2019. "High-order Hidden Markov Model for trend prediction in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 1-12.
- Zhiguang (Gerald) Wang, 2009. "Volatility Risk," Issue Briefs 2009513, South Dakota State University, Department of Economics.
- Lu-Tao Zhao & Li-Na Liu & Zi-Jie Wang & Ling-Yun He, 2019. "Forecasting Oil Price Volatility in the Era of Big Data: A Text Mining for VaR Approach," Sustainability, MDPI, vol. 11(14), pages 1-20, July.
- Chiroma, Haruna & Abdulkareem, Sameem & Herawan, Tutut, 2015. "Evolutionary Neural Network model for West Texas Intermediate crude oil price prediction," Applied Energy, Elsevier, vol. 142(C), pages 266-273.
- Zhao, Lu-Tao & Wang, Yi & Guo, Shi-Qiu & Zeng, Guan-Rong, 2018. "A novel method based on numerical fitting for oil price trend forecasting," Applied Energy, Elsevier, vol. 220(C), pages 154-163.
- Ahrens, W. Ashley & Sharma, Vijaya R., 1997. "Trends in Natural Resource Commodity Prices: Deterministic or Stochastic?," Journal of Environmental Economics and Management, Elsevier, vol. 33(1), pages 59-74, May.
- Huang, Shupei & An, Haizhong & Huang, Xuan & Wang, Yue, 2018. "Do all sectors respond to oil price shocks simultaneously?," Applied Energy, Elsevier, vol. 227(C), pages 393-402.
- Noguera, José, 2013. "Oil prices: Breaks and trends," Energy Economics, Elsevier, vol. 37(C), pages 60-67.
- Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992.
"Simple Technical Trading Rules and the Stochastic Properties of Stock Returns,"
Journal of Finance, American Finance Association, vol. 47(5), pages 1731-1764, December.
- Brock, W. & Lakonishok, J. & Lebaron, B., 1991. "Simple Technical Trading Rules And The Stochastic Properties Of Stock Returns," Working papers 90-22, Wisconsin Madison - Social Systems.
- Wang, Minggang & Chen, Ying & Tian, Lixin & Jiang, Shumin & Tian, Zihao & Du, Ruijin, 2016. "Fluctuation behavior analysis of international crude oil and gasoline price based on complex network perspective," Applied Energy, Elsevier, vol. 175(C), pages 109-127.
- Christiane Baumeister & Lutz Kilian, 2014.
"What Central Bankers Need To Know About Forecasting Oil Prices,"
International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 55, pages 869-889, August.
- Baumeister, Christiane & Kilian, Lutz, 2012. "What Central Bankers Need to Know about Forecasting Oil Prices," CEPR Discussion Papers 9118, C.E.P.R. Discussion Papers.
- Christiane Baumeister & Lutz Kilian, 2013. "What Central Bankers Need to Know about Forecasting Oil Prices," Staff Working Papers 13-15, Bank of Canada.
- Diebold, Francis X & Mariano, Roberto S, 2002.
"Comparing Predictive Accuracy,"
Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
- Diebold, Francis X & Mariano, Roberto S, 1995. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 253-263, July.
- Francis X. Diebold & Roberto S. Mariano, 1994. "Comparing Predictive Accuracy," NBER Technical Working Papers 0169, National Bureau of Economic Research, Inc.
- Gao, Xiangyun & Fang, Wei & An, Feng & Wang, Yue, 2017. "Detecting method for crude oil price fluctuation mechanism under different periodic time series," Applied Energy, Elsevier, vol. 192(C), pages 201-212.
- Ghosh, Suchismita & Deb, Anish & Sarkar, Gautam, 2016. "Taylor series approach for function approximation using ‘estimated’ higher derivatives," Applied Mathematics and Computation, Elsevier, vol. 284(C), pages 89-101.
- Zhang, Yue-Jun & Wu, Yao-Bin, 2019. "The time-varying spillover effect between WTI crude oil futures returns and hedge funds," International Review of Economics & Finance, Elsevier, vol. 61(C), pages 156-169.
- Snudden, Stephen, 2018. "Targeted growth rates for long-horizon crude oil price forecasts," International Journal of Forecasting, Elsevier, vol. 34(1), pages 1-16.
- Naser, Hanan, 2016. "Estimating and forecasting the real prices of crude oil: A data rich model using a dynamic model averaging (DMA) approach," Energy Economics, Elsevier, vol. 56(C), pages 75-87.
- Dbouk, Wassim & Jamali, Ibrahim, 2018. "Predicting daily oil prices: Linear and non-linear models," Research in International Business and Finance, Elsevier, vol. 46(C), pages 149-165.
- Fan, Liwei & Pan, Sijia & Li, Zimin & Li, Huiping, 2016. "An ICA-based support vector regression scheme for forecasting crude oil prices," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 245-253.
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- Zhao, Lu-Tao & Zheng, Zhi-Yi & Wei, Yi-Ming, 2023. "Forecasting oil inventory changes with Google trends: A hybrid wavelet decomposer and ARDL-SVR ensemble model," Energy Economics, Elsevier, vol. 120(C).
- Yu-Wei Chen & Chui-Yu Chiu & Mu-Chun Hsiao, 2021. "An Auxiliary Index for Reducing Brent Crude Investment Risk—Evaluating the Price Relationships between Brent Crude and Commodities," Sustainability, MDPI, vol. 13(9), pages 1-45, April.
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Keywords
Time-varying characteristic; Trend threshold; Time-varying trend decomposition model;All these keywords.
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