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Impact of financial instability on international crude oil volatility: New sight from a regime-switching framework

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  • Hong, Yanran
  • Wang, Lu
  • Liang, Chao
  • Umar, Muhammad
Abstract
In this paper, we investigate the dynamic impact of financial stress on crude oil volatility by employing a time-varying transition probabilities Markov regime-switching GARCH model (TVTP-MS-GARCH). Different from the existing work, we mainly consider the possible nonlinearity and regime changes among crude oil volatility and financial stress. First, the in-sample results strongly support the existence of the potential regime switches among the two financial fundamentals. Second, compared with the symmetric TVTP-MS-GARCH model, the model based on an asymmetric framework shows a better predictive performance in the out-of-sample findings. It implies that the dynamic impact of financial stress on crude oil exhibits an asymmetric feature. Finally, our findings are robust to several alternative checks, including other lags of financial stress and estimated window size. Thus, it is necessary to focus on the dynamic changes of financial stress for accurately predicting crude oil volatility.

Suggested Citation

  • Hong, Yanran & Wang, Lu & Liang, Chao & Umar, Muhammad, 2022. "Impact of financial instability on international crude oil volatility: New sight from a regime-switching framework," Resources Policy, Elsevier, vol. 77(C).
    • Handle: RePEc:eee:jrpoli:v:77:y:2022:i:c:s0301420722001155
    DOI: 10.1016/j.resourpol.2022.102667
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    1. Barbara Rossi & Atsushi Inoue, 2012. "Out-of-Sample Forecast Tests Robust to the Choice of Window Size," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(3), pages 432-453, April.
    2. Kilian, Lutz, 2022. "Understanding the estimation of oil demand and oil supply elasticities," Energy Economics, Elsevier, vol. 107(C).
    3. Jammazi, Rania, 2012. "Oil shock transmission to stock market returns: Wavelet-multivariate Markov switching GARCH approach," Energy, Elsevier, vol. 37(1), pages 430-454.
    4. Lutz Kilian & Cheolbeom Park, 2009. "The Impact Of Oil Price Shocks On The U.S. Stock Market," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(4), pages 1267-1287, November.
    5. Pesaran, M. Hashem & Timmermann, Allan, 2009. "Testing Dependence Among Serially Correlated Multicategory Variables," Journal of the American Statistical Association, American Statistical Association, vol. 104(485), pages 325-337.
    6. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    7. Sarwar, Suleman & Tiwari, Aviral Kumar & Tingqiu, Cao, 2020. "Analyzing volatility spillovers between oil market and Asian stock markets," Resources Policy, Elsevier, vol. 66(C).
    8. Nazlioglu, Saban & Soytas, Ugur & Gupta, Rangan, 2015. "Oil prices and financial stress: A volatility spillover analysis," Energy Policy, Elsevier, vol. 82(C), pages 278-288.
    9. Balli, Esra & Nazif Çatık, Abdurrahman & Nugent, Jeffrey B., 2021. "Time-varying impact of oil shocks on trade balances: Evidence using the TVP-VAR model," Energy, Elsevier, vol. 217(C).
    10. John Y. Campbell & Samuel B. Thompson, 2008. "Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1509-1531, July.
    11. Bouri, Elie, 2015. "Return and volatility linkages between oil prices and the Lebanese stock market in crisis periods," Energy, Elsevier, vol. 89(C), pages 365-371.
    12. Liu, Min & Lee, Chien-Chiang, 2021. "Capturing the dynamics of the China crude oil futures: Markov switching, co-movement, and volatility forecasting," Energy Economics, Elsevier, vol. 103(C).
    13. Gupta, Rangan & Kanda, Patrick & Tiwari, Aviral Kumar & Wohar, Mark E., 2019. "Time-varying predictability of oil market movements over a century of data: The role of US financial stress," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    14. Liu, Renren & Chen, Jianzhong & Wen, Fenghua, 2021. "The nonlinear effect of oil price shocks on financial stress: Evidence from China," The North American Journal of Economics and Finance, Elsevier, vol. 55(C).
    15. Nimantha Manamperi, 2015. "A Comparative Analysis on US Financial Stress Indicators," International Journal of Economics and Financial Issues, Econjournals, vol. 5(2), pages 613-623.
    16. Jamel Jouini & Wajih Khallouli, 2019. "Regime switching in the reactions of stock markets in Saudi Arabia to oil price variations," The World Economy, Wiley Blackwell, vol. 42(8), pages 2467-2506, August.
    17. Chao Liang & Yu Wei & Xiafei Li & Xuhui Zhang & Yifeng Zhang, 2020. "Uncertainty and crude oil market volatility: new evidence," Applied Economics, Taylor & Francis Journals, vol. 52(27), pages 2945-2959, May.
    18. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    19. Gkillas, Konstantinos & Gupta, Rangan & Pierdzioch, Christian, 2020. "Forecasting realized oil-price volatility: The role of financial stress and asymmetric loss," Journal of International Money and Finance, Elsevier, vol. 104(C).
    20. Gao, Xiangyun & Fang, Wei & An, Feng & Wang, Yue, 2017. "Detecting method for crude oil price fluctuation mechanism under different periodic time series," Applied Energy, Elsevier, vol. 192(C), pages 201-212.
    21. Phillip J. Monin, 2019. "The OFR Financial Stress Index," Risks, MDPI, vol. 7(1), pages 1-21, February.
    22. Vo, Minh T., 2009. "Regime-switching stochastic volatility: Evidence from the crude oil market," Energy Economics, Elsevier, vol. 31(5), pages 779-788, September.
    23. 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.
    24. Sarwar, Suleman & Shahbaz, Muhammad & Anwar, Awais & Tiwari, Aviral Kumar, 2019. "The importance of oil assets for portfolio optimization: The analysis of firm level stocks," Energy Economics, Elsevier, vol. 78(C), pages 217-234.
    25. Wang, Yudong & Wu, Chongfeng & Yang, Li, 2016. "Forecasting crude oil market volatility: A Markov switching multifractal volatility approach," International Journal of Forecasting, Elsevier, vol. 32(1), pages 1-9.
    26. 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.
    27. Lammerding, Marc & Stephan, Patrick & Trede, Mark & Wilfling, Bernd, 2013. "Speculative bubbles in recent oil price dynamics: Evidence from a Bayesian Markov-switching state-space approach," Energy Economics, Elsevier, vol. 36(C), pages 491-502.
    28. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    29. Hung, Jui-Cheng & Yi-Hsien Wang, & Chang, Matthew C. & Shih, Kuang-Hsun & Hsiu-Hsueh Kao,, 2011. "Minimum variance hedging with bivariate regime-switching model for WTI crude oil," Energy, Elsevier, vol. 36(5), pages 3050-3057.
    30. Franc Klaassen, 2002. "Improving GARCH volatility forecasts with regime-switching GARCH," Empirical Economics, Springer, vol. 27(2), pages 363-394.
    31. Garcia, Rene, 1998. "Asymptotic Null Distribution of the Likelihood Ratio Test in Markov Switching Models," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(3), pages 763-788, August.
    32. Ma, Feng & Wahab, M.I.M. & Huang, Dengshi & Xu, Weiju, 2017. "Forecasting the realized volatility of the oil futures market: A regime switching approach," Energy Economics, Elsevier, vol. 67(C), pages 136-145.
    33. Lu, Feng-bin & Hong, Yong-miao & Wang, Shou-yang & Lai, Kin-keung & Liu, John, 2014. "Time-varying Granger causality tests for applications in global crude oil markets," Energy Economics, Elsevier, vol. 42(C), pages 289-298.
    34. 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.
    35. Chen, Wang & Hamori, Shigeyuki & Kinkyo, Takuji, 2014. "Macroeconomic impacts of oil prices and underlying financial shocks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 29(C), pages 1-12.
    36. Wan, Jer-Yuh & Kao, Chung-Wei, 2015. "Interactions between oil and financial markets — Do conditions of financial stress matter?," Energy Economics, Elsevier, vol. 52(PA), pages 160-175.
    37. Khalfaoui, Rabeh & Baumöhl, Eduard & Sarwar, Suleman & Výrost, Tomáš, 2021. "Connectedness between energy and nonenergy commodity markets: Evidence from quantile coherency networks," Resources Policy, Elsevier, vol. 74(C).
    38. Sadorsky, Perry, 2006. "Modeling and forecasting petroleum futures volatility," Energy Economics, Elsevier, vol. 28(4), pages 467-488, July.
    39. 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.
    40. Henry, Ólan T., 2009. "Regime switching in the relationship between equity returns and short-term interest rates in the UK," Journal of Banking & Finance, Elsevier, vol. 33(2), pages 405-414, February.
    41. Craig S. Hakkio & William R. Keeton, 2009. "Financial stress: what is it, how can it be measured, and why does it matter?," Economic Review, Federal Reserve Bank of Kansas City, vol. 94(Q II), pages 5-50.
    42. Illing, Mark & Liu, Ying, 2006. "Measuring financial stress in a developed country: An application to Canada," Journal of Financial Stability, Elsevier, vol. 2(3), pages 243-265, October.
    43. Herrera, Ana María & Hu, Liang & Pastor, Daniel, 2018. "Forecasting crude oil price volatility," International Journal of Forecasting, Elsevier, vol. 34(4), pages 622-635.
    44. Barrow, Devon K. & Crone, Sven F., 2016. "Cross-validation aggregation for combining autoregressive neural network forecasts," International Journal of Forecasting, Elsevier, vol. 32(4), pages 1120-1137.
    45. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333.
    46. Baris Kocaarslan & Ugur Soytas & Ramazan Sari & Ecenur Ugurlu, 2019. "The Changing Role of Financial Stress, Oil Price, and Gold Price in Financial Contagion among US and BRIC Markets," International Review of Finance, International Review of Finance Ltd., vol. 19(3), pages 541-574, September.
    47. Lu, Xinjie & Ma, Feng & Wang, Jiqian & Wang, Jianqiong, 2020. "Examining the predictive information of CBOE OVX on China’s oil futures volatility: Evidence from MS-MIDAS models," Energy, Elsevier, vol. 212(C).
    48. Muhammad Shafiullah & Sajid M. Chaudhry & Muhammad Shahbaz & Juan C. Reboredo, 2021. "Quantile causality and dependence between crude oil and precious metal prices," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(4), pages 6264-6280, October.
    49. Wang, Lu & Ma, Feng & Hao, Jianyang & Gao, Xinxin, 2021. "Forecasting crude oil volatility with geopolitical risk: Do time-varying switching probabilities play a role?," International Review of Financial Analysis, Elsevier, vol. 76(C).
    50. Jammazi, Rania & Ferrer, Román & Jareño, Francisco & Shahzad, Syed Jawad Hussain, 2017. "Time-varying causality between crude oil and stock markets: What can we learn from a multiscale perspective?," International Review of Economics & Finance, Elsevier, vol. 49(C), pages 453-483.
    51. Onur POLAT, 2018. "The Interaction between Oil Price and Financial Stress: Evidence from the U.S. Data," Fiscaoeconomia, Tubitak Ulakbim JournalPark (Dergipark), issue 3.
    52. Luo, Jiawen & Ji, Qiang & Klein, Tony & Todorova, Neda & Zhang, Dayong, 2020. "On realized volatility of crude oil futures markets: Forecasting with exogenous predictors under structural breaks," Energy Economics, Elsevier, vol. 89(C).
    53. Wang, Lu & Ma, Feng & Liu, Jing & Yang, Lin, 2020. "Forecasting stock price volatility: New evidence from the GARCH-MIDAS model," International Journal of Forecasting, Elsevier, vol. 36(2), pages 684-694.
    54. Chao Liang & Feng Ma & Lu Wang & Qing Zeng, 2021. "The information content of uncertainty indices for natural gas futures volatility forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(7), pages 1310-1324, November.
    55. Filardo, Andrew J, 1994. "Business-Cycle Phases and Their Transitional Dynamics," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 299-308, July.
    56. Lin Huang & Dayong Zhang, 2015. "Hedging or Speculation: What Can We Learn from the Volume-Return Relationship?," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 51(6), pages 1117-1128, November.
    57. Kevin L. Kliesen & Michael T. Owyang & E. Katarina Vermann, 2012. "Disentangling diverse measures: a survey of financial stress indexes," Review, Federal Reserve Bank of St. Louis, issue Sep, pages 369-398.
    58. Nademi, Arash & Nademi, Younes, 2018. "Forecasting crude oil prices by a semiparametric Markov switching model: OPEC, WTI, and Brent cases," Energy Economics, Elsevier, vol. 74(C), pages 757-766.
    59. Niţoi, Mihai & Pochea, Maria Miruna, 2020. "Time-varying dependence in European equity markets: A contagion and investor sentiment driven analysis," Economic Modelling, Elsevier, vol. 86(C), pages 133-147.
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    More about this item

    Keywords

    Crude oil volatility; Financial stress; Time-varying transition probability; Markov-switching GARCH; Forecasting efficiency;
    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
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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