Forecasting natural gas prices using highly flexible time-varying parameter models
Author
Abstract
Suggested Citation
Download full text from publisher
References listed on IDEAS
- Apergis, Nicholas & Bowden, Nicholas & Payne, James E., 2015. "Downstream integration of natural gas prices across U.S. states: Evidence from deregulation regime shifts," Energy Economics, Elsevier, vol. 49(C), pages 82-92.
- Shi, Xunpeng & Variam, Hari M.P., 2017. "East Asia’s gas-market failure and distinctive economics—A case study of low oil prices," Applied Energy, Elsevier, vol. 195(C), pages 800-809.
- Chan, Joshua C.C., 2013.
"Moving average stochastic volatility models with application to inflation forecast,"
Journal of Econometrics, Elsevier, vol. 176(2), pages 162-172.
- Joshua C C Chan, 2012. "Moving Average Stochastic Volatility Models with Application to Inflation Forecast," ANU Working Papers in Economics and Econometrics 2012-591, Australian National University, College of Business and Economics, School of Economics.
- Joshua C.C. Chan, 2013. "Moving Average Stochastic Volatility Models with Application to Inflation Forecast," CAMA Working Papers 2013-31, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998.
"Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models,"
The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(3), pages 361-393.
- Sangjoon Kim, Neil Shephard & Siddhartha Chib, "undated". "Stochastic volatility: likelihood inference and comparison with ARCH models," Economics Papers W26, revised version of W, Economics Group, Nuffield College, University of Oxford.
- Sangjoon Kim & Neil Shephard, 1994. "Stochastic volatility: likelihood inference and comparison with ARCH models," Economics Papers 3., Economics Group, Nuffield College, University of Oxford.
- Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1996. "Stochastic Volatility: Likelihood Inference And Comparison With Arch Models," Econometrics 9610002, University Library of Munich, Germany.
- Carriero, Andrea & Galvão, Ana Beatriz & Kapetanios, George, 2019. "A comprehensive evaluation of macroeconomic forecasting methods," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1226-1239.
- Batten, Jonathan A. & Ciner, Cetin & Lucey, Brian M., 2017. "The dynamic linkages between crude oil and natural gas markets," Energy Economics, Elsevier, vol. 62(C), pages 155-170.
- Wang, Yudong & Liu, Li & Wu, Chongfeng, 2017. "Forecasting the real prices of crude oil using forecast combinations over time-varying parameter models," Energy Economics, Elsevier, vol. 66(C), pages 337-348.
- Hamid Abrishami & Vida Varahrami, 2011. "Different methods for gas price forecasting," Cuadernos de Economía - Spanish Journal of Economics and Finance, Asociación Cuadernos de Economía, vol. 34(96), pages 137-144, Diciembre.
- John Geweke & Gianni Amisano, 2011.
"Hierarchical Markov normal mixture models with applications to financial asset returns,"
Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(1), pages 1-29, January/F.
- Amisano, Gianni & Geweke, John, 2007. "Hierarchical Markov normal mixture models with applications to financial asset returns," Working Paper Series 831, European Central Bank.
- John Geweke & Gianni Amisano, 2007. "Hierarchical Markov Normal Mixture Models with Applications to Financial Asset Returns," Working Papers 0705, University of Brescia, Department of Economics.
- Wakamatsu, Hiroki & Aruga, Kentaka, 2013. "The impact of the shale gas revolution on the U.S. and Japanese natural gas markets," Energy Policy, Elsevier, vol. 62(C), pages 1002-1009.
- Caporin, Massimiliano & Fontini, Fulvio, 2017.
"The long-run oil–natural gas price relationship and the shale gas revolution,"
Energy Economics, Elsevier, vol. 64(C), pages 511-519.
- Massimiliano Caporin & Fulvio Fontini, 2015. "The Long-Run Oil-Natural Gas Price Relationship And The Shale Gas Revolution," "Marco Fanno" Working Papers 0198, Dipartimento di Scienze Economiche "Marco Fanno".
- James H. Stock & Mark W. Watson, 2007.
"Why Has U.S. Inflation Become Harder to Forecast?,"
Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
- James H. Stock & Mark W. Watson, 2006. "Why Has U.S. Inflation Become Harder to Forecast?," NBER Working Papers 12324, National Bureau of Economic Research, Inc.
- Alizadeh, Amir H. & Nomikos, Nikos K. & Pouliasis, Panos K., 2008. "A Markov regime switching approach for hedging energy commodities," Journal of Banking & Finance, Elsevier, vol. 32(9), pages 1970-1983, September.
- Todd E. Clark & Francesco Ravazzolo, 2015. "Macroeconomic Forecasting Performance under Alternative Specifications of Time‐Varying Volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(4), pages 551-575, June.
- Wang, TianTian & Zhang, Dayong & Clive Broadstock, David, 2019. "Financialization, fundamentals, and the time-varying determinants of US natural gas prices," Energy Economics, Elsevier, vol. 80(C), pages 707-719.
- Vipin Arora and Jozef Lieskovsky, 2014.
"Natural Gas and U.S. Economic Activity,"
The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
- Arora, Vipin & Lieskovsky, Jozef, 2012. "Natural Gas and U.S. Economic Activity," MPRA Paper 42659, University Library of Munich, Germany.
- Vo, Minh T., 2009. "Regime-switching stochastic volatility: Evidence from the crude oil market," Energy Economics, Elsevier, vol. 31(5), pages 779-788, September.
- Zhang, Dayong & Wang, Tiantian & Shi, Xunpeng & Liu, Jia, 2018. "Is hub-based pricing a better choice than oil indexation for natural gas? Evidence from a multiple bubble test," Energy Economics, Elsevier, vol. 76(C), pages 495-503.
- Gary Koop & Simon M. Potter, 2007. "Estimation and Forecasting in Models with Multiple Breaks," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 74(3), pages 763-789.
- Fong, Wai Mun & See, Kim Hock, 2002. "A Markov switching model of the conditional volatility of crude oil futures prices," Energy Economics, Elsevier, vol. 24(1), pages 71-95, January.
- Bastianin, Andrea & Galeotti, Marzio & Polo, Michele, 2019.
"Convergence of European natural gas prices,"
Energy Economics, Elsevier, vol. 81(C), pages 793-811.
- Andrea, Bastianin & Marzio, Galeotti & Michele, Polo, 2018. "Convergence of European natural gas prices," Working Papers 394, University of Milano-Bicocca, Department of Economics, revised 06 Dec 2018.
- Geweke, J, 1993. "Bayesian Treatment of the Independent Student- t Linear Model," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(S), pages 19-40, Suppl. De.
- Di Sanzo, Silvestro, 2018. "A Markov switching long memory model of crude oil price return volatility," Energy Economics, Elsevier, vol. 74(C), pages 351-359.
- Hou, Chenghan & Nguyen, Bao H., 2018. "Understanding the US natural gas market: A Markov switching VAR approach," Energy Economics, Elsevier, vol. 75(C), pages 42-53.
- Moting Su & Zongyi Zhang & Ye Zhu & Donglan Zha, 2019. "Data-Driven Natural Gas Spot Price Forecasting with Least Squares Regression Boosting Algorithm," Energies, MDPI, vol. 12(6), pages 1-13, March.
- Balcilar, Mehmet & Ozdemir, Zeynel Abidin, 2019. "The nexus between the oil price and its volatility risk in a stochastic volatility in the mean model with time-varying parameters," Resources Policy, Elsevier, vol. 61(C), pages 572-584.
- James H. Stock & Mark W. Watson, 2007. "Erratum to "Why Has U.S. Inflation Become Harder to Forecast?"," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
- Nguyen, Hang T. & Nabney, Ian T., 2010. "Short-term electricity demand and gas price forecasts using wavelet transforms and adaptive models," Energy, Elsevier, vol. 35(9), pages 3674-3685.
- Joshua C C Chan & Cody Y L Hsiao, 2013. "Estimation of Stochastic Volatility Models with Heavy Tails and Serial Dependence," CAMA Working Papers 2013-74, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- Anna Creti & Duc Khuong Nguyen, 2015. "Energy markets׳ financialization, risk spillovers, and pricing models," Post-Print hal-01517413, HAL.
- Buchanan, W. K. & Hodges, P. & Theis, J., 2001. "Which way the natural gas price: an attempt to predict the direction of natural gas spot price movements using trader positions," Energy Economics, Elsevier, vol. 23(3), pages 279-293, May.
- Chib, Siddhartha, 1996. "Calculating posterior distributions and modal estimates in Markov mixture models," Journal of Econometrics, Elsevier, vol. 75(1), pages 79-97, November.
- repec:dau:papers:123456789/14774 is not listed on IDEAS
- Kosater, Peter & Mosler, Karl, 2006.
"Can Markov regime-switching models improve power-price forecasts? Evidence from German daily power prices,"
Applied Energy, Elsevier, vol. 83(9), pages 943-958, September.
- Kosater, Peter & Mosler, Karl, 2005. "Can Markov-regime switching models improve power price forecasts? Evidence for German daily power prices," Discussion Papers in Econometrics and Statistics 1/05, University of Cologne, Institute of Econometrics and Statistics.
- Stern, Jonathan, 2014. "International gas pricing in Europe and Asia: A crisis of fundamentals," Energy Policy, Elsevier, vol. 64(C), pages 43-48.
- Mishra, Vinod & Smyth, Russell, 2016. "Are natural gas spot and futures prices predictable?," Economic Modelling, Elsevier, vol. 54(C), pages 178-186.
- MacAvoy, Paul W. & Moshkin, Nickolay V., 2000. "The new long-term trend in the price of natural gas," Resource and Energy Economics, Elsevier, vol. 22(4), pages 315-338, October.
- Hailemariam, Abebe & Smyth, Russell, 2019. "What drives volatility in natural gas prices?," Energy Economics, Elsevier, vol. 80(C), pages 731-742.
- Wiggins, Seth & Etienne, Xiaoli L., 2017. "Turbulent times: Uncovering the origins of US natural gas price fluctuations since deregulation," Energy Economics, Elsevier, vol. 64(C), pages 196-205.
- Chenghan Hou & Bao H. Nguyen, 2018. "Understanding the US natural gas market: A Markov switching VAR approach," CAMA Working Papers 2018-14, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- Andrew Atkeson & Lee E. Ohanian, 2001. "Are Phillips curves useful for forecasting inflation?," Quarterly Review, Federal Reserve Bank of Minneapolis, vol. 25(Win), pages 2-11.
- Chang, Kuang-Liang, 2012. "Volatility regimes, asymmetric basis effects and forecasting performance: An empirical investigation of the WTI crude oil futures market," Energy Economics, Elsevier, vol. 34(1), pages 294-306.
- Zhang, Dayong & Ji, Qiang, 2018. "Further evidence on the debate of oil-gas price decoupling: A long memory approach," Energy Policy, Elsevier, vol. 113(C), pages 68-75.
- Zhang, Dayong & Shi, Min & Shi, Xunpeng, 2018. "Oil indexation, market fundamentals, and natural gas prices: An investigation of the Asian premium in natural gas trade," Energy Economics, Elsevier, vol. 69(C), pages 33-41.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Ferrari, Davide & Ravazzolo, Francesco & Vespignani, Joaquin, 2021.
"Forecasting energy commodity prices: A large global dataset sparse approach,"
Energy Economics, Elsevier, vol. 98(C).
- Davide Ferrari & Francesco Ravazzolo & Joaquin Vespignani, 2019. "Forecasting energy commodity prices: A large global dataset sparse approach," CAMA Working Papers 2019-90, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- Davide Ferrari & Francesco Ravazzolo & Joaquin Vespignani, 2021. "Forecasting Energy Commodity Prices: A Large Global Dataset Sparse Approach," BEMPS - Bozen Economics & Management Paper Series BEMPS83, Faculty of Economics and Management at the Free University of Bozen.
- Ferrari, Davide & Ravazzolo, Francesco & Vespignani, Joaquin, 2019. "Forecasting energy commodity prices: a large global dataset sparse approach," Working Papers 2019-09, University of Tasmania, Tasmanian School of Business and Economics.
- Davide Ferrari & Francesco Ravazzolo & Joaquin Vespignani, 2019. "Forecasting Energy Commodity Prices: A Large Global Dataset Sparse Approach," Working Papers No 11/2019, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
- Davide Ferrari & Francesco Ravazzolo & Joaquin L. Vespignani, 2019. "Forecasting Energy Commodity Prices: A Large Global Dataset Sparse Approach," Globalization Institute Working Papers 376, Federal Reserve Bank of Dallas.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Gao, Shen & Hou, Chenghan & Nguyen, Bao H., 2021. "Forecasting natural gas prices using highly flexible time-varying parameter models," Economic Modelling, Elsevier, vol. 105(C).
- Szafranek Karol & Rubaszek Michał, 2024.
"Have European natural gas prices decoupled from crude oil prices? Evidence from TVP-VAR analysis,"
Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 28(3), pages 507-530.
- Michał Rubaszek & Karol Szafranek, 2022. "Have European natural gas prices decoupled from crude oil prices? Evidence from TVP-VAR analysis," KAE Working Papers 2022-078, Warsaw School of Economics, Collegium of Economic Analysis.
- Wang, Tiantian & Zhang, Dayong & Ji, Qiang & Shi, Xunpeng, 2020. "Market reforms and determinants of import natural gas prices in China," Energy, Elsevier, vol. 196(C).
- Rubaszek, Michał & Uddin, Gazi Salah, 2020. "The role of underground storage in the dynamics of the US natural gas market: A threshold model analysis," Energy Economics, Elsevier, vol. 87(C).
- Miao, Xiaoyu & Wang, Qunwei & Dai, Xingyu, 2022. "Is oil-gas price decoupling happening in China? A multi-scale quantile-on-quantile approach," International Review of Economics & Finance, Elsevier, vol. 77(C), pages 450-470.
- Wang, TianTian & Zhang, Dayong & Clive Broadstock, David, 2019. "Financialization, fundamentals, and the time-varying determinants of US natural gas prices," Energy Economics, Elsevier, vol. 80(C), pages 707-719.
- Joshua C. C. Chan, 2019. "Large Bayesian vector autoregressions," CAMA Working Papers 2019-19, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- Jan Prüser, 2021. "Forecasting US inflation using Markov dimension switching," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(3), pages 481-499, April.
- Joshua C. C. Chan, 2017.
"The Stochastic Volatility in Mean Model With Time-Varying Parameters: An Application to Inflation Modeling,"
Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 35(1), pages 17-28, January.
- Joshua C.C. Chan, 2015. "The Stochastic Volatility in Mean Model with Time-Varying Parameters: An Application to Inflation Modeling," CAMA Working Papers 2015-07, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- Wang, Tiantian & Qu, Wan & Zhang, Dayong & Ji, Qiang & Wu, Fei, 2022. "Time-varying determinants of China's liquefied natural gas import price: A dynamic model averaging approach," Energy, Elsevier, vol. 259(C).
- Cross, Jamie & Poon, Aubrey, 2016. "Forecasting structural change and fat-tailed events in Australian macroeconomic variables," Economic Modelling, Elsevier, vol. 58(C), pages 34-51.
- Hailemariam, Abebe & Smyth, Russell, 2019. "What drives volatility in natural gas prices?," Energy Economics, Elsevier, vol. 80(C), pages 731-742.
- Wang, Zuyi & Kim, Man-Keun, 2022. "Price bubbles in oil & gas markets and their transfer," Resources Policy, Elsevier, vol. 79(C).
- Xie, Gang & Jiang, Fuxin & Zhang, Chengyuan, 2023. "A secondary decomposition-ensemble methodology for forecasting natural gas prices using multisource data," Resources Policy, Elsevier, vol. 85(PA).
- Joshua C. C. Chan, 2018.
"Specification tests for time-varying parameter models with stochastic volatility,"
Econometric Reviews, Taylor & Francis Journals, vol. 37(8), pages 807-823, September.
- Joshua C.C. Chan, 2015. "Specification tests for time-varying parameter models with stochastic volatility," CAMA Working Papers 2015-42, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- Chan, Joshua C.C., 2013.
"Moving average stochastic volatility models with application to inflation forecast,"
Journal of Econometrics, Elsevier, vol. 176(2), pages 162-172.
- Joshua C C Chan, 2012. "Moving Average Stochastic Volatility Models with Application to Inflation Forecast," ANU Working Papers in Economics and Econometrics 2012-591, Australian National University, College of Business and Economics, School of Economics.
- Joshua C.C. Chan, 2013. "Moving Average Stochastic Volatility Models with Application to Inflation Forecast," CAMA Working Papers 2013-31, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- Gil-Alana, Luis A. & Gupta, Rangan & Olubusoye, Olusanya E. & Yaya, OlaOluwa S., 2016.
"Time series analysis of persistence in crude oil price volatility across bull and bear regimes,"
Energy, Elsevier, vol. 109(C), pages 29-37.
- Luis A. Gil-Alana & Rangan Gupta & Olusanya E. Olubusoye & OlaOluwa S. Yaya, 2015. "Time Series Analysis of Persistence in Crude Oil Price Volatility across Bull and Bear Regimes," Working Papers 201580, University of Pretoria, Department of Economics.
- Palma, Alessia & Paltrinieri, Andrea & Goodell, John W. & Oriani, Marco Ercole, 2024. "The black box of natural gas market: Past, present, and future," International Review of Financial Analysis, Elsevier, vol. 94(C).
- Theodosios Perifanis & Athanasios Dagoumas, 2020. "Price and Volatility Spillovers between Crude Oil and Natural Gas markets in Europe and Japan-Korea," International Journal of Energy Economics and Policy, Econjournals, vol. 10(5), pages 432-446.
- Zhang, Lingge & Yang, Dong & Wu, Shining & Luo, Meifeng, 2023. "Revisiting the pricing benchmarks for Asian LNG — An equilibrium analysis," Energy, Elsevier, vol. 262(PA).
More about this item
Keywords
natural gas price; structural breaks; forecasting; time-varying pa- rameter; Markov switching; stochastic volatility.;All these keywords.
JEL classification:
- C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
- E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
- Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
NEP fields
This paper has been announced in the following NEP Reports:- NEP-ENE-2020-04-06 (Energy Economics)
- NEP-FOR-2020-04-06 (Forecasting)
- NEP-MAC-2020-04-06 (Macroeconomics)
- NEP-ORE-2020-04-06 (Operations Research)
- NEP-REG-2020-04-06 (Regulation)
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:tas:wpaper:32412. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Oscar Pavlov (email available below). General contact details of provider: https://edirc.repec.org/data/dutasau.html .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.