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The role of coronavirus news in the volatility forecasting of crude oil futures markets: Evidence from China

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  • Niu, Zibo
  • Liu, Yuanyuan
  • Gao, Wang
  • Zhang, Hongwei

Abstract

Based on the high-frequency heterogeneous autoregressive (HAR) model, this paper investigates whether coronavirus news (in China and globally) contains incremental information to predict the volatility of China's crude oil, and studies which types of coronavirus news can better forecast China's crude oil volatility. Considering the information overlap among various coronavirus news items and making full use of the information in various coronavirus news items, this paper uses two prevailing shrinkage methods, lasso and elastic nets, to select coronavirus news items and then uses the HAR model to predict China's crude oil volatility. The results show that (i) coronavirus news can be utilized to significantly predict China's crude oil volatility for both in-sample and out-of-sample analyses; (ii) the Panic Index (PI) and the Country Sentiment Index (CSI) have a greater impact on China's crude oil volatility. Additionally, China's Fake News Index (FNI) have a significant impact on China's crude oil volatility forecast; and (iii) global coronavirus news provides more incremental information than China's coronavirus news for predicting the volatility of China's crude oil market, which indicates that global coronavirus news is also a key factor to consider when predicting the market volatility of China's crude oil.

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  • Niu, Zibo & Liu, Yuanyuan & Gao, Wang & Zhang, Hongwei, 2021. "The role of coronavirus news in the volatility forecasting of crude oil futures markets: Evidence from China," Resources Policy, Elsevier, vol. 73(C).
    • Handle: RePEc:eee:jrpoli:v:73:y:2021:i:c:s0301420721001872
    DOI: 10.1016/j.resourpol.2021.102173
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    as
    1. Jiahan Li & Ilias Tsiakas & Wei Wang, 2015. "Predicting Exchange Rates Out of Sample: Can Economic Fundamentals Beat the Random Walk?," Journal of Financial Econometrics, Oxford University Press, vol. 13(2), pages 293-341.
    2. Baek, Seungho & Mohanty, Sunil K. & Glambosky, Mina, 2020. "COVID-19 and stock market volatility: An industry level analysis," Finance Research Letters, Elsevier, vol. 37(C).
    3. 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.
    4. Narayan, Paresh Kumar, 2019. "Can stale oil price news predict stock returns?," Energy Economics, Elsevier, vol. 83(C), pages 430-444.
    5. Arouri, Mohamed El Hédi & Lahiani, Amine & Lévy, Aldo & Nguyen, Duc Khuong, 2012. "Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models," Energy Economics, Elsevier, vol. 34(1), pages 283-293.
    6. Gharib, Cheima & Mefteh-Wali, Salma & Jabeur, Sami Ben, 2021. "The bubble contagion effect of COVID-19 outbreak: Evidence from crude oil and gold markets," Finance Research Letters, Elsevier, vol. 38(C).
    7. Paul C. Tetlock & Maytal Saar‐Tsechansky & Sofus Macskassy, 2008. "More Than Words: Quantifying Language to Measure Firms' Fundamentals," Journal of Finance, American Finance Association, vol. 63(3), pages 1437-1467, June.
    8. Marshall, Andrew & Musayev, Taleh & Pinto, Helena & Tang, Leilei, 2012. "Impact of news announcements on the foreign exchange implied volatility," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(4), pages 719-737.
    9. Salisu, Afees A. & Akanni, Lateef & Raheem, Ibrahim, 2020. "The COVID-19 global fear index and the predictability of commodity price returns," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    10. 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.
    11. Andersen, Torben G. & Dobrev, Dobrislav & Schaumburg, Ernst, 2012. "Jump-robust volatility estimation using nearest neighbor truncation," Journal of Econometrics, Elsevier, vol. 169(1), pages 75-93.
    12. Sévi, Benoît, 2014. "Forecasting the volatility of crude oil futures using intraday data," European Journal of Operational Research, Elsevier, vol. 235(3), pages 643-659.
    13. Torben G. Andersen & Tim Bollerslev & Francis X. Diebold & Paul Labys, 2003. "Modeling and Forecasting Realized Volatility," Econometrica, Econometric Society, vol. 71(2), pages 579-625, March.
    14. Narayan, Paresh Kumar & Narayan, Seema, 2007. "Modelling oil price volatility," Energy Policy, Elsevier, vol. 35(12), pages 6549-6553, December.
    15. Schmidbauer, Harald & Rösch, Angi, 2012. "OPEC news announcements: Effects on oil price expectation and volatility," Energy Economics, Elsevier, vol. 34(5), pages 1656-1663.
    16. Loughran, Tim & McDonald, Bill & Pragidis, Ioannis, 2019. "Assimilation of oil news into prices," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 105-118.
    17. Ole E. Barndorff-Nielsen, 2004. "Power and Bipower Variation with Stochastic Volatility and Jumps," Journal of Financial Econometrics, Oxford University Press, vol. 2(1), pages 1-37.
    18. Scott R Baker & Nicholas Bloom & Steven J Davis & Kyle Kost & Marco Sammon & Tasaneeya Viratyosin & Jeffrey Pontiff, 0. "The Unprecedented Stock Market Reaction to COVID-19," The Review of Asset Pricing Studies, Society for Financial Studies, vol. 10(4), pages 742-758.
    19. Zhang, Yaojie & Ma, Feng & Wang, Yudong, 2019. "Forecasting crude oil prices with a large set of predictors: Can LASSO select powerful predictors?," Journal of Empirical Finance, Elsevier, vol. 54(C), pages 97-117.
    20. Smales, Lee A., 2014. "News sentiment and the investor fear gauge," Finance Research Letters, Elsevier, vol. 11(2), pages 122-130.
    21. Andrew J. Patton & Kevin Sheppard, 2015. "Good Volatility, Bad Volatility: Signed Jumps and The Persistence of Volatility," The Review of Economics and Statistics, MIT Press, vol. 97(3), pages 683-697, July.
    22. Rangel, José Gonzalo, 2011. "Macroeconomic news, announcements, and stock market jump intensity dynamics," Journal of Banking & Finance, Elsevier, vol. 35(5), pages 1263-1276, May.
    23. Shi, Yanlin & Ho, Kin-Yip, 2021. "News sentiment and states of stock return volatility: Evidence from long memory and discrete choice models," Finance Research Letters, Elsevier, vol. 38(C).
    24. Christopher J. Neely & David E. Rapach & Jun Tu & Guofu Zhou, 2014. "Forecasting the Equity Risk Premium: The Role of Technical Indicators," Management Science, INFORMS, vol. 60(7), pages 1772-1791, July.
    25. Siem Jan Koopman & André Lucas & Marcel Scharth, 2016. "Predicting Time-Varying Parameters with Parameter-Driven and Observation-Driven Models," The Review of Economics and Statistics, MIT Press, vol. 98(1), pages 97-110, March.
    26. Luc Bauwens & Edoardo Otranto, 2016. "Modeling the Dependence of Conditional Correlations on Market Volatility," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 254-268, April.
    27. Chew Lian Chua & Sarantis Tsiaplias, 2019. "Information flows and stock market volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 34(1), pages 129-148, January.
    28. Liang, Chao & Tang, Linchun & Li, Yan & Wei, Yu, 2020. "Which sentiment index is more informative to forecast stock market volatility? Evidence from China," International Review of Financial Analysis, Elsevier, vol. 71(C).
    29. Zhang, Dayong & Hu, Min & Ji, Qiang, 2020. "Financial markets under the global pandemic of COVID-19," Finance Research Letters, Elsevier, vol. 36(C).
    30. Jones, Charles M. & Lamont, Owen & Lumsdaine, Robin L., 1998. "Macroeconomic news and bond market volatility," Journal of Financial Economics, Elsevier, vol. 47(3), pages 315-337, March.
    31. Cepoi, Cosmin-Octavian, 2020. "Asymmetric dependence between stock market returns and news during COVID-19 financial turmoil," Finance Research Letters, Elsevier, vol. 36(C).
    32. Francesco Audrino & Simon D. Knaus, 2016. "Lassoing the HAR Model: A Model Selection Perspective on Realized Volatility Dynamics," Econometric Reviews, Taylor & Francis Journals, vol. 35(8-10), pages 1485-1521, December.
    33. Veronica Guerrieri & Guido Lorenzoni & Ludwig Straub & Iván Werning, 2022. "Macroeconomic Implications of COVID-19: Can Negative Supply Shocks Cause Demand Shortages?," American Economic Review, American Economic Association, vol. 112(5), pages 1437-1474, May.
    34. Crego, Julio A., 2020. "Why does public news augment information asymmetries?," Journal of Financial Economics, Elsevier, vol. 137(1), pages 72-89.
    35. Li, Xuerong & Shang, Wei & Wang, Shouyang, 2019. "Text-based crude oil price forecasting: A deep learning approach," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1548-1560.
    36. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    37. 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.
    38. Kang, Sang Hoon & Yoon, Seong-Min, 2013. "Modeling and forecasting the volatility of petroleum futures prices," Energy Economics, Elsevier, vol. 36(C), pages 354-362.
    39. 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.
    40. Bakas, Dimitrios & Triantafyllou, Athanasios, 2020. "Commodity price volatility and the economic uncertainty of pandemics," Economics Letters, Elsevier, vol. 193(C).
    41. Ole E. Barndorff-Nielsen & Peter Reinhard Hansen & Asger Lunde & Neil Shephard, 2008. "Designing Realized Kernels to Measure the ex post Variation of Equity Prices in the Presence of Noise," Econometrica, Econometric Society, vol. 76(6), pages 1481-1536, November.
    42. Xin Huang & George Tauchen, 2005. "The Relative Contribution of Jumps to Total Price Variance," Journal of Financial Econometrics, Oxford University Press, vol. 3(4), pages 456-499.
    43. Broadstock, David C. & Zhang, Dayong, 2019. "Social-media and intraday stock returns: The pricing power of sentiment," Finance Research Letters, Elsevier, vol. 30(C), pages 116-123.
    44. Engle, Robert F & Ng, Victor K, 1993. "Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-1778, December.
    45. Torben G. Andersen & Tim Bollerslev & Nour Meddahi, 2004. "Analytical Evaluation Of Volatility Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 45(4), pages 1079-1110, November.
    46. Sadorsky, Perry, 2006. "Modeling and forecasting petroleum futures volatility," Energy Economics, Elsevier, vol. 28(4), pages 467-488, July.
    47. Gupta, Kartick & Banerjee, Rajabrata, 2019. "Does OPEC news sentiment influence stock returns of energy firms in the United States?," Energy Economics, Elsevier, vol. 77(C), pages 34-45.
    48. Brandt, Michael W. & Gao, Lin, 2019. "Macro fundamentals or geopolitical events? A textual analysis of news events for crude oil," Journal of Empirical Finance, Elsevier, vol. 51(C), pages 64-94.
    49. Ewing, Bradley T. & Malik, Farooq, 2017. "Modelling asymmetric volatility in oil prices under structural breaks," Energy Economics, Elsevier, vol. 63(C), pages 227-233.
    50. Diego García, 2013. "Sentiment during Recessions," Journal of Finance, American Finance Association, vol. 68(3), pages 1267-1300, June.
    51. Adekoya, Oluwasegun B. & Oliyide, Johnson A., 2021. "How COVID-19 drives connectedness among commodity and financial markets: Evidence from TVP-VAR and causality-in-quantiles techniques," Resources Policy, Elsevier, vol. 70(C).
    52. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
    53. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    54. Zhang, Wenting & Hamori, Shigeyuki, 2021. "Crude oil market and stock markets during the COVID-19 pandemic: Evidence from the US, Japan, and Germany," International Review of Financial Analysis, Elsevier, vol. 74(C).
    55. 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.
    56. Yang, Cai & Gong, Xu & Zhang, Hongwei, 2019. "Volatility forecasting of crude oil futures: The role of investor sentiment and leverage effect," Resources Policy, Elsevier, vol. 61(C), pages 548-563.
    57. Blitz, David & Huisman, Rob & Swinkels, Laurens & van Vliet, Pim, 2020. "Media attention and the volatility effect," Finance Research Letters, Elsevier, vol. 36(C).
    58. Demirer, RIza & Kutan, Ali M., 2010. "The behavior of crude oil spot and futures prices around OPEC and SPR announcements: An event study perspective," Energy Economics, Elsevier, vol. 32(6), pages 1467-1476, November.
    59. Zhu, Xuehong & Zhang, Hongwei & Zhong, Meirui, 2017. "Volatility forecasting using high frequency data: The role of after-hours information and leverage effects," Resources Policy, Elsevier, vol. 54(C), pages 58-70.
    60. Maslyuk-Escobedo, Svetlana & Rotaru, Kristian & Dokumentov, Alexander, 2017. "News sentiment and jumps in energy spot and futures markets," Pacific-Basin Finance Journal, Elsevier, vol. 45(C), pages 186-210.
    61. Wilms, Ines & Rombouts, Jeroen & Croux, Christophe, 2021. "Multivariate volatility forecasts for stock market indices," International Journal of Forecasting, Elsevier, vol. 37(2), pages 484-499.
    62. Bomfim, Antulio N., 2003. "Pre-announcement effects, news effects, and volatility: Monetary policy and the stock market," Journal of Banking & Finance, Elsevier, vol. 27(1), pages 133-151, January.
    63. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    64. Mensi, Walid & Hammoudeh, Shawkat & Yoon, Seong-Min, 2014. "How do OPEC news and structural breaks impact returns and volatility in crude oil markets? Further evidence from a long memory process," Energy Economics, Elsevier, vol. 42(C), pages 343-354.
    65. Umit G. Gurun & Alexander W. Butler, 2012. "Don't Believe the Hype: Local Media Slant, Local Advertising, and Firm Value," Journal of Finance, American Finance Association, vol. 67(2), pages 561-598, April.
    66. Jiang, George J. & Konstantinidi, Eirini & Skiadopoulos, George, 2012. "Volatility spillovers and the effect of news announcements," Journal of Banking & Finance, Elsevier, vol. 36(8), pages 2260-2273.
    67. Yang, Xiaolan & Zhu, Yu & Cheng, Teng Yuan, 2020. "How the individual investors took on big data: The effect of panic from the internet stock message boards on stock price crash," Pacific-Basin Finance Journal, Elsevier, vol. 59(C).
    68. Ji, Qiang & Zhang, Dayong, 2019. "China’s crude oil futures: Introduction and some stylized facts," Finance Research Letters, Elsevier, vol. 28(C), pages 376-380.
    69. Hongwei Zhang & Xuehong Zhu & Yaoqi Guo & Haibo Liu, 2018. "A separate reduced‐form volatility forecasting model for nonferrous metal market: Evidence from copper and aluminum," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(7), pages 754-766, November.
    70. Zhang, Yaojie & Wei, Yu & Zhang, Yi & Jin, Daxiang, 2019. "Forecasting oil price volatility: Forecast combination versus shrinkage method," Energy Economics, Elsevier, vol. 80(C), pages 423-433.
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