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Modeling and Forecasting Commodity Market Volatility with Long-term Economic and Financial Variables

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  • Thomas Walther

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  • Duc Khuong Nguyen

Abstract

This paper investigates the time-varying volatility patterns of some major commodities as well as the potential factors that drive their long-term volatility component. For this purpose, we make use of a recently proposed GARCH-MIDAS approach which typically allows us to examine the role of economic and financial variables of different frequencies. Using commodity futures for Crude Oil (WTI and Brent), Gold, Silver and Platinum as well as a commodity index, our results show the necessity of disentangling the short-term and long-term components in modeling and forecasting commodity volatility. They also indicate that the long-term volatility of most commodity futures is significantly driven by the level of the global real economic activity as well as the changes in consumer sentiment, industrial production, and economic policy uncertainty. However, the forecasting results are not alike across commodity futures as no single model fits all commodities.

Suggested Citation

  • Thomas Walther & Duc Khuong Nguyen, 2018. "Modeling and Forecasting Commodity Market Volatility with Long-term Economic and Financial Variables," Working Papers on Finance 1824, University of St. Gallen, School of Finance.
  • Handle: RePEc:usg:sfwpfi:2018:24
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    as
    1. Opschoor, Anne & van Dijk, Dick & van der Wel, Michel, 2014. "Predicting volatility and correlations with Financial Conditions Indexes," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 435-447.
    2. Smales, L.A., 2017. "Commodity market volatility in the presence of U.S. and Chinese macroeconomic news," Journal of Commodity Markets, Elsevier, vol. 7(C), pages 15-27.
    3. Büyükşahin, Bahattin & Robe, Michel A., 2014. "Speculators, commodities and cross-market linkages," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 38-70.
    4. Walther, Thomas & Klein, Tony & Thu, Hien Pham & Piontek, Krzysztof, 2017. "True or spurious long memory in European non-EMU currencies," Research in International Business and Finance, Elsevier, vol. 40(C), pages 217-230.
    5. Karali, Berna & Ramirez, Octavio A., 2014. "Macro determinants of volatility and volatility spillover in energy markets," Energy Economics, Elsevier, vol. 46(C), pages 413-421.
    6. Baur, Dirk G. & McDermott, Thomas K., 2010. "Is gold a safe haven? International evidence," Journal of Banking & Finance, Elsevier, vol. 34(8), pages 1886-1898, August.
    7. Bahloul, Walid & Bouri, Abdelfettah, 2016. "The impact of investor sentiment on returns and conditional volatility in U.S. futures markets," Journal of Multinational Financial Management, Elsevier, vol. 36(C), pages 89-102.
    8. Narayan, Paresh Kumar & Narayan, Seema & Sharma, Susan Sunila, 2013. "An analysis of commodity markets: What gain for investors?," Journal of Banking & Finance, Elsevier, vol. 37(10), pages 3878-3889.
    9. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    10. Baumeister, Christiane & Guérin, Pierre & Kilian, Lutz, 2015. "Do high-frequency financial data help forecast oil prices? The MIDAS touch at work," International Journal of Forecasting, Elsevier, vol. 31(2), pages 238-252.
    11. Dirk G. Baur & Brian M. Lucey, 2010. "Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold," The Financial Review, Eastern Finance Association, vol. 45(2), pages 217-229, May.
    12. El Hedi Arouri, Mohamed & Jouini, Jamel & Nguyen, Duc Khuong, 2011. "Volatility spillovers between oil prices and stock sector returns: Implications for portfolio management," Journal of International Money and Finance, Elsevier, vol. 30(7), pages 1387-1405.
    13. Lutz Kilian & Clara Vega, 2011. "Do Energy Prices Respond to U.S. Macroeconomic News? A Test of the Hypothesis of Predetermined Energy Prices," The Review of Economics and Statistics, MIT Press, vol. 93(2), pages 660-671, May.
    14. Lucey, Brian M. & Sharma, Susan Sunila & Vigne, Samuel A., 2017. "Gold and inflation(s) – A time-varying relationship," Economic Modelling, Elsevier, vol. 67(C), pages 88-101.
    15. Adams, Zeno & Glück, Thorsten, 2015. "Financialization in commodity markets: A passing trend or the new normal?," Journal of Banking & Finance, Elsevier, vol. 60(C), pages 93-111.
    16. Robert F. Engle & Eric Ghysels & Bumjean Sohn, 2013. "Stock Market Volatility and Macroeconomic Fundamentals," The Review of Economics and Statistics, MIT Press, vol. 95(3), pages 776-797, July.
    17. Schwert, G William, 1989. " Why Does Stock Market Volatility Change over Time?," Journal of Finance, American Finance Association, vol. 44(5), pages 1115-1153, December.
    18. Filis, George & Degiannakis, Stavros & Floros, Christos, 2011. "Dynamic correlation between stock market and oil prices: The case of oil-importing and oil-exporting countries," International Review of Financial Analysis, Elsevier, vol. 20(3), pages 152-164, June.
    19. Christian Conrad & Karin Loch, 2015. "Anticipating Long‐Term Stock Market Volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(7), pages 1090-1114, November.
    20. Berna Karali & Gabriel J. Power, 2013. "Short- and Long-Run Determinants of Commodity Price Volatility," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 95(3), pages 724-738.
    21. Klein, Tony, 2017. "Dynamic correlation of precious metals and flight-to-quality in developed markets," Finance Research Letters, Elsevier, vol. 23(C), pages 283-290.
    22. Narayan, Paresh Kumar & Sharma, Susan Sunila, 2011. "New evidence on oil price and firm returns," Journal of Banking & Finance, Elsevier, vol. 35(12), pages 3253-3262.
    23. Daskalaki, Charoula & Kostakis, Alexandros & Skiadopoulos, George, 2014. "Are there common factors in individual commodity futures returns?," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 346-363.
    24. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    25. 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.
    26. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    27. Hossein Asgharian & Ai Jun Hou & Farrukh Javed, 2013. "The Importance of the Macroeconomic Variables in Forecasting Stock Return Variance: A GARCH‐MIDAS Approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 32(7), pages 600-612, November.
    28. Robert S. Pindyck, 2004. "Volatility and commodity price dynamics," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 24(11), pages 1029-1047, November.
    29. Batten, Jonathan A. & Ciner, Cetin & Lucey, Brian M., 2010. "The macroeconomic determinants of volatility in precious metals markets," Resources Policy, Elsevier, vol. 35(2), pages 65-71, June.
    30. Yin, Libo & Zhou, Yimin, 2016. "What drives long-term oil market volatility? Fundamentals versus speculation," Economics - The Open-Access, Open-Assessment E-Journal, Kiel Institute for the World Economy (IfW), vol. 10, pages 1-26.
    31. Silvennoinen, Annastiina & Thorp, Susan, 2013. "Financialization, crisis and commodity correlation dynamics," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 24(C), pages 42-65.
    32. Robert B. Barsky & Lutz Kilian, 2002. "Do We Really Know that Oil Caused the Great Stagflation? A Monetary Alternative," NBER Chapters, in: NBER Macroeconomics Annual 2001, Volume 16, pages 137-198, National Bureau of Economic Research, Inc.
    33. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    34. Bekiros, Stelios & Nguyen, Duc Khuong & Sandoval Junior, Leonidas & Uddin, Gazi Salah, 2017. "Information diffusion, cluster formation and entropy-based network dynamics in equity and commodity markets," European Journal of Operational Research, Elsevier, vol. 256(3), pages 945-961.
    35. Belén Nieto & Alfonso Novales & Gonzalo Rubio, 2015. "Macroeconomic and Financial Determinants of the Volatility of Corporate Bond Returns," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 5(04), pages 1-41, December.
    36. Libo Yin, 2016. "Does oil price respond to macroeconomic uncertainty? New evidence," Empirical Economics, Springer, vol. 51(3), pages 921-938, November.
    37. Emiliano Magrini & Ayca Donmez, 2013. "Agricultural Commodity Price Volatility and Its Macroeconomic Determinants: A GARCH-MIDAS Approach," JRC Working Papers JRC84138, Joint Research Centre (Seville site).
    38. Wang, Fangfang & Ghysels, Eric, 2015. "Econometric Analysis Of Volatility Component Models," Econometric Theory, Cambridge University Press, vol. 31(2), pages 362-393, April.
    39. Conrad, Christian & Loch, Karin & Rittler, Daniel, 2014. "On the macroeconomic determinants of long-term volatilities and correlations in U.S. stock and crude oil markets," Journal of Empirical Finance, Elsevier, vol. 29(C), pages 26-40.
    40. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    41. Christophe Pérignon & R.D. Smith, 2008. "A New Approach to Comparing VaR Estimation Methods," Post-Print hal-00854087, HAL.
    42. Robert F. Engle & Jose Gonzalo Rangel, 2008. "The Spline-GARCH Model for Low-Frequency Volatility and Its Global Macroeconomic Causes," Review of Financial Studies, Society for Financial Studies, vol. 21(3), pages 1187-1222, May.
    43. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    44. Pan, Zhiyuan & Wang, Yudong & Wu, Chongfeng & Yin, Libo, 2017. "Oil price volatility and macroeconomic fundamentals: A regime switching GARCH-MIDAS model," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 130-142.
    45. Daskalaki, Charoula & Skiadopoulos, George, 2011. "Should investors include commodities in their portfolios after all? New evidence," Journal of Banking & Finance, Elsevier, vol. 35(10), pages 2606-2626, October.
    46. Dietrich Domanski & Alexandra Heath, 2007. "Financial investors and commodity markets," BIS Quarterly Review, Bank for International Settlements, March.
    47. Adams, Zeno & Glueck, Thorsten, 2014. "Financialization in Commodity Markets: A Passing Trend or the New Normal?," Working Papers on Finance 1413, University of St. Gallen, School of Finance, revised Aug 2015.
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    Cited by:

    1. Omid Faseli, 2020. "The relationship between European Brent crude oil price development and the US macroeconomy," International Journal of Research in Business and Social Science (2147-4478), Center for the Strategic Studies in Business and Finance, vol. 9(1), pages 80-87, January.
    2. Peng-Fei Dai & Xiong Xiong & Wei-Xing Zhou, 2020. "The role of global economic policy uncertainty in predicting crude oil futures volatility: Evidence from a two-factor GARCH-MIDAS model," Papers 2007.12838, arXiv.org.
    3. Krzysztof Drachal, 2018. "Determining Time-Varying Drivers of Spot Oil Price in a Dynamic Model Averaging Framework," Energies, MDPI, Open Access Journal, vol. 11(5), pages 1-24, May.
    4. Thomas Walther & Tony Klein, 2018. "Exogenous Drivers of Cryptocurrency Volatility - A Mixed Data Sampling Approach To Forecasting," Working Papers on Finance 1815, University of St. Gallen, School of Finance.
    5. Afees A. Salisu & Rangan Gupta & Elie Bouri & Qiang Ji, 2020. "Forecasting Oil Volatility Using a GARCH-MIDAS Approach: The Role of Global Economic Conditions," Working Papers 202051, University of Pretoria, Department of Economics.
    6. Miroslava Zavadska & Lucía Morales & Joseph Coughlan, 2018. "The Lead–Lag Relationship between Oil Futures and Spot Prices—A Literature Review," International Journal of Financial Studies, MDPI, Open Access Journal, vol. 6(4), pages 1-22, October.

    More about this item

    Keywords

    Commodity futures; GARCH; Long-term volatility; Macroeconomic effects; Mixed data sampling.;

    JEL classification:

    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • Q02 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Commodity Market

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