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Forecasting energy commodity prices: A large global dataset sparse approach

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  • Davide Ferrari
  • Francesco Ravazzolo
  • Joaquin Vespignani

Abstract

This paper focuses on forecasting quarterly energy prices of commodities, such as oil, gas and coal, using the Global VAR dataset proposed by Mohaddes and Raissi (2018). This dataset includes a number of potentially informative quarterly macroeconomic variables for the 33 largest economies, overall accounting for more than 80% of the global GDP. To deal with the information on this large database, we apply a dynamic factor model based on a penalized maximum likelihood approach that allows to shrink parameters to zero and to estimate sparse factor loadings. The estimated latent factors show considerable sparsity and heterogeneity in the selected loadings across variables. When the model is extended to predict energy commodity prices up to four periods ahead, results indicate larger predictability relative to the benchmark random walk model for 1-quarter ahead for all energy commodities. In our application, the largest improvement in terms of prediction accuracy is observed when predicting gas prices from 1 to 4 quarters ahead.

Suggested Citation

  • 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.
  • Handle: RePEc:een:camaaa:2019-90
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    1. Roberto Casarin & Stefano Grassi & Francesco Ravazzolo & Herman K. van Dijk, 2015. "Dynamic predictive density combinations for large data sets in economics and finance," Working Paper 2015/12, Norges Bank.
    2. Kang, Wensheng & Ratti, Ronald A. & Vespignani, Joaquin, 2016. "The impact of oil price shocks on the U.S. stock market: A note on the roles of U.S. and non-U.S. oil production," Economics Letters, Elsevier, vol. 145(C), pages 176-181.
    3. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2008. "Nowcasting: The real-time informational content of macroeconomic data," Journal of Monetary Economics, Elsevier, vol. 55(4), pages 665-676, May.
    4. A. Anzuini & M. J. Lombardi & P. Pagano, 2013. "The Impact of Monetary Policy Shocks on Commodity Prices," International Journal of Central Banking, International Journal of Central Banking, vol. 9(3), pages 125-150, September.
    5. 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.
    6. Diaz, Elena Maria & Molero, Juan Carlos & Perez de Gracia, Fernando, 2016. "Oil price volatility and stock returns in the G7 economies," Energy Economics, Elsevier, vol. 54(C), pages 417-430.
    7. Lombardi, Marco J. & Ravazzolo, Francesco, 2016. "On the correlation between commodity and equity returns: Implications for portfolio allocation," Journal of Commodity Markets, Elsevier, vol. 2(1), pages 45-57.
    8. Hilde C. Bjørnland & Vegard H. Larsen & Junior Maih, 2018. "Oil and Macroeconomic (In)stability," American Economic Journal: Macroeconomics, American Economic Association, vol. 10(4), pages 128-151, October.
    9. Mauro Bernardi & Leopoldo Catania, 2014. "The Model Confidence Set package for R," Papers 1410.8504, arXiv.org.
    10. Baumeister, Christiane & Korobilis, Dimitris & Lee, Thomas K., 2020. "Energy Markets and Global Economic Conditions," CEPR Discussion Papers 14580, C.E.P.R. Discussion Papers.
    11. Christiane Baumeister & Lutz Kilian, 2015. "Forecasting the Real Price of Oil in a Changing World: A Forecast Combination Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 338-351, July.
    12. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
    13. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2012. "A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models," The Review of Economics and Statistics, MIT Press, vol. 94(4), pages 1014-1024, November.
    14. Ratti, Ronald A. & Vespignani, Joaquin L., 2015. "Commodity prices and BRIC and G3 liquidity: A SFAVEC approach," Journal of Banking & Finance, Elsevier, vol. 53(C), pages 18-33.
    15. Cologni, Alessandro & Manera, Matteo, 2008. "Oil prices, inflation and interest rates in a structural cointegrated VAR model for the G-7 countries," Energy Economics, Elsevier, vol. 30(3), pages 856-888, May.
    16. Baffes, John, 2007. "Oil spills on other commodities," Resources Policy, Elsevier, vol. 32(3), pages 126-134, September.
    17. James H. Stock & Mark W. Watson, 2012. "Disentangling the Channels of the 2007-09 Recession," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 43(1 (Spring), pages 81-156.
    18. Mr. Kenneth Rogoff & Mr. Yu-chin Chen, 2002. "Commodity Currencies and Empirical Exchange Rate Puzzles," IMF Working Papers 2002/027, International Monetary Fund.
    19. Bjørnland, Hilde C. & Ravazzolo, Francesco & Thorsrud, Leif Anders, 2017. "Forecasting GDP with global components: This time is different," International Journal of Forecasting, Elsevier, vol. 33(1), pages 153-173.
    20. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    21. Hulshof, Daan & van der Maat, Jan-Pieter & Mulder, Machiel, 2016. "Market fundamentals, competition and natural-gas prices," Energy Policy, Elsevier, vol. 94(C), pages 480-491.
    22. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
    23. Catherine Doz & Domenico Giannone & Lucrezia Reichlin, 2012. "A Quasi Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models," Post-Print hal-00638440, HAL.
    24. Knut Are Aastveit & Hilde C. Bjørnland & Leif Anders Thorsrud, 2015. "What Drives Oil Prices? Emerging Versus Developed Economies," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(7), pages 1013-1028, November.
    25. Giannone, Domenico & Reichlin, Lucrezia & Small, David, 2005. "Nowcasting GDP and Inflation: The Real Time Informational Content of Macroeconomic Data Releases," CEPR Discussion Papers 5178, C.E.P.R. Discussion Papers.
    26. Jean Boivin & Serena Ng, 2005. "Understanding and Comparing Factor-Based Forecasts," International Journal of Central Banking, International Journal of Central Banking, vol. 1(3), December.
    27. Jushan Bai & Peng Wang, 2016. "Econometric Analysis of Large Factor Models," Annual Review of Economics, Annual Reviews, vol. 8(1), pages 53-80, October.
    28. Chen, Yu-chin & Rogoff, Kenneth, 2003. "Commodity currencies," Journal of International Economics, Elsevier, vol. 60(1), pages 133-160, May.
    29. Gao, Shen & Hou, Chenghan & Nguyen, Bao H., 2020. "Forecasting natural gas prices using highly flexible time-varying parameter models," Working Papers 2020-01, University of Tasmania, Tasmanian School of Business and Economics.
    30. Alameer, Zakaria & Fathalla, Ahmed & Li, Kenli & Ye, Haiwang & Jianhua, Zhang, 2020. "Multistep-ahead forecasting of coal prices using a hybrid deep learning model," Resources Policy, Elsevier, vol. 65(C).
    31. Sydney C. Ludvigson & Serena Ng, 2009. "Macro Factors in Bond Risk Premia," Review of Financial Studies, Society for Financial Studies, vol. 22(12), pages 5027-5067, December.
    32. Mauro Bernardi & Leopoldo Catania, 2018. "The model confidence set package for R," International Journal of Computational Economics and Econometrics, Inderscience Enterprises Ltd, vol. 8(2), pages 144-158.
    33. Alquist, Ron & Bhattarai, Saroj & Coibion, Olivier, 2020. "Commodity-price comovement and global economic activity," Journal of Monetary Economics, Elsevier, vol. 112(C), pages 41-56.
    34. Matyjaszek, Marta & Riesgo Fernández, Pedro & Krzemień, Alicja & Wodarski, Krzysztof & Fidalgo Valverde, Gregorio, 2019. "Forecasting coking coal prices by means of ARIMA models and neural networks, considering the transgenic time series theory," Resources Policy, Elsevier, vol. 61(C), pages 283-292.
    35. Caldara, Dario & Cavallo, Michele & Iacoviello, Matteo, 2019. "Oil price elasticities and oil price fluctuations," Journal of Monetary Economics, Elsevier, vol. 103(C), pages 1-20.
    36. Francesco Ravazzolo & Tommy Sveen & Sepideh K. Zahiri, 2016. "Commodity Futures and Forecasting Commodity Currencies," Working Papers No 7/2016, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School.
    37. Shen Gao & Chenghan Hou & Bao H. Nguyen, 2020. "Forecasting natural gas prices using highly flexible time-varying parameter models," CAMA Working Papers 2020-30, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
    38. Hammoudeh, Shawkat & Reboredo, Juan C., 2018. "Oil price dynamics and market-based inflation expectations," Energy Economics, Elsevier, vol. 75(C), pages 484-491.
    39. Charles L. Evans & Jonas D. M. Fisher, 2011. "What are the implications of rising commodity prices for inflation and monetary policy?," Chicago Fed Letter, Federal Reserve Bank of Chicago, issue May.
    40. Claudia Foroni & Francesco Ravazzolo & Pinho J. Ribeiro, 2015. "Forecasting commodity currencies: the role of fundamentals with short-lived predictive content," Working Paper 2015/14, Norges Bank.
    41. Ephraim Leibtag, 2009. "How Much and How Quick? Pass through of Commodity and Input Cost Changes to Retail Food Prices," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 91(5), pages 1462-1467.
    42. James H. Stock & Mark W. Watson, 2012. "Disentangling the Channels of the 2007-09 Recession," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 44(1 (Spring), pages 81-156.
    43. James H. Stock & Mark W. Watson, 2012. "Disentangling the Channels of the 2007-2009 Recession," NBER Working Papers 18094, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Christiane Baumeister & Dimitris Korobilis & Thomas K. Lee, 2020. "Energy Markets and Global Economic Conditions," CESifo Working Paper Series 8282, CESifo.

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    More about this item

    Keywords

    Energy Prices; Forecasting; Dynamic Factor model; Sparse Estimation; Penalized Maximum Likelihood;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • E3 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

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