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On the influence of the U.S. monetary policy on the crude oil price volatility

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  • Amendola, Alessandra
  • Candila, Vincenzo
  • Scognamillo, Antonio

Abstract

Modeling crude oil volatility is of substantial interest for both energy researchers and policy makers. This paper aims to investigate the impact of the U.S. monetary policy on crude oil future price (COFP) volatility. By means of the recently proposed generalized autoregressive conditional hetroskedasticity mixed data sampling (GARCH-MIDAS) model, a proxy of the U.S. monetary policy is included into the COFP volatility equation, alongside with other macroeconomic determinants. Strong evidence that an expansionary monetary policy is associated with an increase of the COFP volatility is found. In particular, an expansionary (restrictive) variation in monetary policy anticipates a positive (negative) variation in COFP volatility. Furthermore, an out of sample forecasting procedure shows that the estimated GARCH-MIDAS model has a superior predictive ability with respect to that of the GARCH(1,1), when the U.S. monetary policy exhibits severe changes in the run-up period.

Suggested Citation

  • Amendola, Alessandra & Candila, Vincenzo & Scognamillo, Antonio, 2015. "On the influence of the U.S. monetary policy on the crude oil price volatility," 2015 Fourth Congress, June 11-12, 2015, Ancona, Italy 207860, Italian Association of Agricultural and Applied Economics (AIEAA).
  • Handle: RePEc:ags:aiea15:207860
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    as
    1. Ron Alquist & Lutz Kilian, 2010. "What do we learn from the price of crude oil futures?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(4), pages 539-573.
    2. 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.
    3. Chernov, Mikhail & Ronald Gallant, A. & Ghysels, Eric & Tauchen, George, 2003. "Alternative models for stock price dynamics," Journal of Econometrics, Elsevier, vol. 116(1-2), pages 225-257.
    4. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2005. "There is a risk-return trade-off after all," Journal of Financial Economics, Elsevier, vol. 76(3), pages 509-548, June.
    5. Robert S. Pindyck, 2004. "Volatility and commodity price dynamics," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 24(11), pages 1029-1047, November.
    6. Richard Luger, 2004. "Exact Tests of Equal Forecast Accuracy with an Application to the Term Structure of Interest Rates," Staff Working Papers 04-2, Bank of Canada.
    7. Kiseok Lee & Shawn Ni & Ronald A. Ratti, 1995. "Oil Shocks and the Macroeconomy: The Role of Price Variability," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4), pages 39-56.
    8. Ben S. Bernanke & Mark Gertler, 2001. "Should Central Banks Respond to Movements in Asset Prices?," American Economic Review, American Economic Association, vol. 91(2), pages 253-257, May.
    9. Ding, Zhuanxin & Granger, Clive W. J., 1996. "Modeling volatility persistence of speculative returns: A new approach," Journal of Econometrics, Elsevier, vol. 73(1), pages 185-215, July.
    10. 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.
    11. Bauwens Luc & Storti Giuseppe, 2009. "A Component GARCH Model with Time Varying Weights," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 13(2), pages 1-33, May.
    12. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    13. A. Ronald Gallant & Chien-Te Hsu & George Tauchen, 1999. "Using Daily Range Data To Calibrate Volatility Diffusions And Extract The Forward Integrated Variance," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 617-631, November.
    14. Dean Croushore & Tom Stark, 2003. "A Real-Time Data Set for Macroeconomists: Does the Data Vintage Matter?," The Review of Economics and Statistics, MIT Press, vol. 85(3), pages 605-617, August.
    15. Sadorsky, Perry, 2006. "Modeling and forecasting petroleum futures volatility," Energy Economics, Elsevier, vol. 28(4), pages 467-488, July.
    16. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    17. Raffaella Giacomini & Halbert White, 2006. "Tests of Conditional Predictive Ability," Econometrica, Econometric Society, vol. 74(6), pages 1545-1578, November.
    18. Gargano, Antonio & Timmermann, Allan, 2014. "Forecasting commodity price indexes using macroeconomic and financial predictors," International Journal of Forecasting, Elsevier, vol. 30(3), pages 825-843.
    19. Zagaglia, Paolo, 2010. "Macroeconomic factors and oil futures prices: A data-rich model," Energy Economics, Elsevier, vol. 32(2), pages 409-417, March.
    20. 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.
    21. Amado, Cristina & Teräsvirta, Timo, 2014. "Modelling changes in the unconditional variance of long stock return series," Journal of Empirical Finance, Elsevier, vol. 25(C), pages 15-35.
    22. Ben S. Bernanke & Mark Gertler, 1999. "Monetary policy and asset price volatility," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 77-128.
    23. He, Yanan & Wang, Shouyang & Lai, Kin Keung, 2010. "Global economic activity and crude oil prices: A cointegration analysis," Energy Economics, Elsevier, vol. 32(4), pages 868-876, July.
    24. 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.
    25. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    26. Sassan Alizadeh & Michael W. Brandt & Francis X. Diebold, 2002. "Range-Based Estimation of Stochastic Volatility Models," Journal of Finance, American Finance Association, vol. 57(3), pages 1047-1091, June.
    27. Bernanke, Ben S & Blinder, Alan S, 1992. "The Federal Funds Rate and the Channels of Monetary Transmission," American Economic Review, American Economic Association, vol. 82(4), pages 901-921, September.
    28. Amado, Cristina & Teräsvirta, Timo, 2013. "Modelling volatility by variance decomposition," Journal of Econometrics, Elsevier, vol. 175(2), pages 142-153.
    29. Tobias Adrian & Joshua Rosenberg, 2008. "Stock Returns and Volatility: Pricing the Short‐Run and Long‐Run Components of Market Risk," Journal of Finance, American Finance Association, vol. 63(6), pages 2997-3030, December.
    30. Narayan, Paresh Kumar & Narayan, Seema, 2007. "Modelling oil price volatility," Energy Policy, Elsevier, vol. 35(12), pages 6549-6553, December.
    31. 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.
    32. Hossein Asgharian & Charlotte Christiansen & Ai Jun Hou, 2016. "Macro-Finance Determinants of the Long-Run Stock–Bond Correlation: The DCC-MIDAS Specification," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 14(3), pages 617-642.
    33. Luís Aguiar-Conraria & Maria Soares, 2011. "Oil and the macroeconomy: using wavelets to analyze old issues," Empirical Economics, Springer, vol. 40(3), pages 645-655, May.
    34. Efimova, Olga & Serletis, Apostolos, 2014. "Energy markets volatility modelling using GARCH," Energy Economics, Elsevier, vol. 43(C), pages 264-273.
    35. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    36. Noureddine Krichene, 2006. "World Crude Oil Markets; Monetary Policy and the Recent Oil Shock," IMF Working Papers 06/62, International Monetary Fund.
    37. Wei, Yu & Wang, Yudong & Huang, Dengshi, 2010. "Forecasting crude oil market volatility: Further evidence using GARCH-class models," Energy Economics, Elsevier, vol. 32(6), pages 1477-1484, November.
    38. Hansen, Peter Reinhard, 2005. "A Test for Superior Predictive Ability," Journal of Business & Economic Statistics, American Statistical Association, vol. 23, pages 365-380, October.
    39. Claudio Borio & William R. White, 2003. "Whither monetary and financial stability : the implications of evolving policy regimes," Proceedings - Economic Policy Symposium - Jackson Hole, Federal Reserve Bank of Kansas City, pages 131-211.
    40. 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.
    41. Agnolucci, Paolo, 2009. "Volatility in crude oil futures: A comparison of the predictive ability of GARCH and implied volatility models," Energy Economics, Elsevier, vol. 31(2), pages 316-321, March.
    42. Fawley, Brett W. & Neely, Christopher J., 2013. "Four stories of quantitative easing," Review, Federal Reserve Bank of St. Louis, issue Jan, pages 51-88.
    43. Tao Wu & Andrew H. McCallum, 2005. "Do oil futures prices help predict future oil prices?," FRBSF Economic Letter, Federal Reserve Bank of San Francisco, issue dec30.
    44. Raffaella Giacomini & Barbara Rossi, 2013. "Forecasting in macroeconomics," Chapters,in: Handbook of Research Methods and Applications in Empirical Macroeconomics, chapter 17, pages 381-408 Edward Elgar Publishing.
    45. Officer, R R, 1973. "The Variability of the Market Factor of the New York Stock Exchange," The Journal of Business, University of Chicago Press, vol. 46(3), pages 434-453, July.
    46. Fama, Eugene F. & French, Kenneth R., 1989. "Business conditions and expected returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 25(1), pages 23-49, November.
    47. 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.
    48. Vivek B. Arora & Martin D. Cerisola, 2000. "How Does U.S. Monetary Policy Influence Economic Conditions in Emerging Markets?," IMF Working Papers 00/148, International Monetary Fund.
    49. Awartani, Basel M.A. & Corradi, Valentina, 2005. "Predicting the volatility of the S&P-500 stock index via GARCH models: the role of asymmetries," International Journal of Forecasting, Elsevier, vol. 21(1), pages 167-183.
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    1. repec:gam:jrisks:v:6:y:2018:i:4:p:116-:d:174522 is not listed on IDEAS
    2. repec:eee:ecmode:v:76:y:2019:i:c:p:135-152 is not listed on IDEAS

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    Keywords

    Agricultural and Food Policy;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E30 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - General (includes Measurement and Data)
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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