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Dynamic Impact of the U.S. Monetary Policy on Oil Market Returns and Volatility

Author

Listed:
  • Hardik A. Marfatia

    () (Department of Economics, Northeastern Illinois University, BBH 344G, 5500 N. St. Louis Ave., Chicago, IL 60625, USA)

  • Rangan Gupta

    () (Department of Economics, University of Pretoria, Pretoria, South Africa)

  • Esin Cakan

    () (Department of Economics, University of New Haven, 300 Boston Post Road, West Haven, CT 06516, USA)

Abstract

In this paper, we assess the dynamic impact of the U.S. monetary policy announcements on oil market futures returns and volatility. We use intra-day data together with a time-varying modeling approach to study the nature of this dynamic impact. In addition, we also control for macroeconomic news shocks and separately study the response of good and bad realized volatility. Evidence suggests that there is a significant time variation in the response of oil returns as well as its volatility to the Federal Reserve policy announcements. Furthermore, we find that higher (lower) uncertainty about Federal Reserve policy actions weakens (strengthens) the impact of the announcements on oil returns and volatility.

Suggested Citation

  • Hardik A. Marfatia & Rangan Gupta & Esin Cakan, 2019. "Dynamic Impact of the U.S. Monetary Policy on Oil Market Returns and Volatility," Working Papers 201916, University of Pretoria, Department of Economics.
  • Handle: RePEc:pre:wpaper:201916
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    References listed on IDEAS

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    1. Ben S. Bernanke & Kenneth N. Kuttner, 2005. "What Explains the Stock Market's Reaction to Federal Reserve Policy?," Journal of Finance, American Finance Association, vol. 60(3), pages 1221-1257, June.
    2. Balcilar, Mehmet & Gupta, Rangan & Wohar, Mark E., 2017. "Common cycles and common trends in the stock and oil markets: Evidence from more than 150years of data," Energy Economics, Elsevier, vol. 61(C), pages 72-86.
    3. Gupta, Rangan & Wohar, Mark, 2017. "Forecasting oil and stock returns with a Qual VAR using over 150years off data," Energy Economics, Elsevier, vol. 62(C), pages 181-186.
    4. James D. Hamilton, 2009. "Causes and Consequences of the Oil Shock of 2007-08," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 40(1 (Spring), pages 215-283.
    5. repec:eee:ecofin:v:45:y:2018:i:c:p:206-214 is not listed on IDEAS
    6. Gupta, Rangan & Yoon, Seong-Min, 2018. "OPEC news and predictability of oil futures returns and volatility: Evidence from a nonparametric causality-in-quantiles approach," The North American Journal of Economics and Finance, Elsevier, vol. 45(C), pages 206-214.
    7. Haugom, Erik & Langeland, Henrik & Molnár, Peter & Westgaard, Sjur, 2014. "Forecasting volatility of the U.S. oil market," Journal of Banking & Finance, Elsevier, vol. 47(C), pages 1-14.
    8. Hausman, Joshua & Wongswan, Jon, 2011. "Global asset prices and FOMC announcements," Journal of International Money and Finance, Elsevier, vol. 30(3), pages 547-571, April.
    9. Jing Cynthia Wu & Fan Dora Xia, 2016. "Measuring the Macroeconomic Impact of Monetary Policy at the Zero Lower Bound," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(2-3), pages 253-291, March.
    10. Marfatia, Hardik A. & Gupta, Rangan & Cakan, Esin, 2017. "The international REIT’s time-varying response to the U.S. monetary policy and macroeconomic surprises," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 640-653.
    11. Balcilar, Mehmet & Gupta, Rangan & Miller, Stephen M., 2015. "Regime switching model of US crude oil and stock market prices: 1859 to 2013," Energy Economics, Elsevier, vol. 49(C), pages 317-327.
    12. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil price realized volatility using information channels from other asset classes," Journal of International Money and Finance, Elsevier, vol. 76(C), pages 28-49.
    13. Drachal, Krzysztof, 2016. "Forecasting spot oil price in a dynamic model averaging framework — Have the determinants changed over time?," Energy Economics, Elsevier, vol. 60(C), pages 35-46.
    14. Bahloul, Walid & Balcilar, Mehmet & Cunado, Juncal & Gupta, Rangan, 2018. "The role of economic and financial uncertainties in predicting commodity futures returns and volatility: Evidence from a nonparametric causality-in-quantiles test," Journal of Multinational Financial Management, Elsevier, vol. 45(C), pages 52-71.
    15. Arabinda Basistha & Alexander Kurov, 2015. "The Impact of Monetary Policy Surprises on Energy Prices," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 35(1), pages 87-103, January.
    16. 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.
    17. repec:eee:appene:v:233-234:y:2019:i::p:612-621 is not listed on IDEAS
    18. Apostolos Serletis, 2012. "Oil Price Uncertainty," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 8407, November.
    19. Arjun Chatrath & Hong Miao & Sanjay Ramchander, 2012. "Does the price of crude oil respond to macroeconomic news?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 32(6), pages 536-559, June.
    20. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, Oxford University Press, vol. 131(4), pages 1593-1636.
    21. Hayo, Bernd & Kutan, Ali M. & Neuenkirch, Matthias, 2012. "Communication matters: US monetary policy and commodity price volatility," Economics Letters, Elsevier, vol. 117(1), pages 247-249.
    22. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    23. James H. Stock & Mark W.Watson, 2003. "Forecasting Output and Inflation: The Role of Asset Prices," Journal of Economic Literature, American Economic Association, vol. 41(3), pages 788-829, September.
    24. Martin Bodenstein & Luca Guerrieri & Lutz Kilian, 2012. "Monetary Policy Responses to Oil Price Fluctuations," IMF Economic Review, Palgrave Macmillan;International Monetary Fund, vol. 60(4), pages 470-504, December.
    25. Walid Bahloul & Rangan Gupta, 2018. "Impact of macroeconomic news surprises and uncertainty for major economies on returns and volatility of oil futures," International Economics, CEPII research center, issue 156, pages 247-253.
    26. repec:bla:eufman:v:24:y:2018:i:2:p:239-260 is not listed on IDEAS
    27. Rosa, Carlo, 2014. "The high-frequency response of energy prices to U.S. monetary policy: Understanding the empirical evidence," Energy Economics, Elsevier, vol. 45(C), pages 295-303.
    28. Hamilton, James D & Herrera, Ana Maria, 2004. "Oil Shocks and Aggregate Macroeconomic Behavior: The Role of Monetary Policy: Comment," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 36(2), pages 265-286, April.
    29. repec:oup:jfinec:v:14:y:2016:i:1:p:29-80. is not listed on IDEAS
    30. Marfatia, Hardik A., 2015. "Monetary policy's time-varying impact on the US bond markets: Role of financial stress and risks," The North American Journal of Economics and Finance, Elsevier, vol. 34(C), pages 103-123.
    31. Marcel Prokopczuk & Lazaros Symeonidis & Chardin Wese Simen, 2016. "Do Jumps Matter for Volatility Forecasting? Evidence from Energy Markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(8), pages 758-792, August.
    32. Mehmet Balcilar & Esin Cakan & Rangan Gupta, 2016. "Does U.S. News Impact Asian Emerging Markets? Evidence from Nonparametric Causality-in-Quantiles Test," Working Papers 201631, University of Pretoria, Department of Economics.
    33. Scotti, Chiara, 2016. "Surprise and uncertainty indexes: Real-time aggregation of real-activity macro-surprises," Journal of Monetary Economics, Elsevier, vol. 82(C), pages 1-19.
    34. repec:bla:intfin:v:20:y:2017:i:3:p:289-316 is not listed on IDEAS
    35. Cooley, Thomas F & Prescott, Edward C, 1976. "Estimation in the Presence of Stochastic Parameter Variation," Econometrica, Econometric Society, vol. 44(1), pages 167-184, January.
    36. Farhad Taghizadeh Hesary & Naoyuki Yoshino, 2014. "Monetary policies and oil price determination: an empirical analysis," OPEC Energy Review, Organization of the Petroleum Exporting Countries, vol. 38(1), pages 1-20, March.
    37. Hamilton, James D, 1983. "Oil and the Macroeconomy since World War II," Journal of Political Economy, University of Chicago Press, vol. 91(2), pages 228-248, April.
    38. repec:oup:qjecon:v:133:y:2018:i:3:p:1283-1330. is not listed on IDEAS
    39. repec:aea:jecper:v:32:y:2018:i:3:p:59-86 is not listed on IDEAS
    40. Naoyuki Yoshino & Farhad Taghizadeh-Hesary, 2014. "Monetary policy and oil price fluctuations following the subprime mortgage crisis," International Journal of Monetary Economics and Finance, Inderscience Enterprises Ltd, vol. 7(3), pages 157-174.
    41. Massimiliano Caporin & Eduardo Rossi & Paolo Santucci de Magistris, 2016. "Volatility Jumps and Their Economic Determinants," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 14(1), pages 29-80.
    42. repec:eee:jbfina:v:86:y:2018:i:c:p:127-142 is not listed on IDEAS
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    More about this item

    Keywords

    Monetary Policy; Macroeconomic Surprises; Oil Returns and Volatility; Time-Varying Model;

    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
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • E52 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit - - - Monetary Policy
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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