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Volatility forecasting of crude oil futures based on a genetic algorithm regularization online extreme learning machine with a forgetting factor: The role of news during the COVID-19 pandemic

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  • Weng, Futian
  • Zhang, Hongwei
  • Yang, Cai

Abstract

The outbreak of news and opinions during the COVID-19 pandemic is unprecedented in this age of rapid dissemination of information. The ensuing uncertainty has led to the emergence of heightened volatility in prices of crude oil futures. Whether such news has predictive value for the volatility of crude oil futures during the COVID-19 pandemic is examined in this research. We proposed a modeling framework, genetic algorithm regularization online extreme learning machine with forgetting factor (GA-RFOS-ELM), to estimate the effects of news during the COVID-19 pandemic on the volatility of crude oil futures. GA-RFOS-ELM could learn block-by-block with fixed or varying block size when considering the block own valid period. The experimental results illustrate that news during the COVID-19 pandemic has more predictive information, which is crucial for short-term volatility forecasting of crude oil futures. The novel approach illustrates that online update learning ability is needed during the COVID-19 pandemic, which could be effective and efficient in volatility forecasting of crude oil futures. The contributions of our study are significant for investors and administrators to predict and understand the behavior of volatility during the COVID-19 pandemic.

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  • Weng, Futian & Zhang, Hongwei & Yang, Cai, 2021. "Volatility forecasting of crude oil futures based on a genetic algorithm regularization online extreme learning machine with a forgetting factor: The role of news during the COVID-19 pandemic," Resources Policy, Elsevier, vol. 73(C).
    • Handle: RePEc:eee:jrpoli:v:73:y:2021:i:c:s0301420721001628
    DOI: 10.1016/j.resourpol.2021.102148
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    as
    1. Zhang, Yue-Jun & Zhang, Lu, 2015. "Interpreting the crude oil price movements: Evidence from the Markov regime switching model," Applied Energy, Elsevier, vol. 143(C), pages 96-109.
    2. Al-Awadhi, Abdullah M. & Alsaifi, Khaled & Al-Awadhi, Ahmad & Alhammadi, Salah, 2020. "Death and contagious infectious diseases: Impact of the COVID-19 virus on stock market returns," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    3. Zhang, Dayong & Hu, Min & Ji, Qiang, 2020. "Financial markets under the global pandemic of COVID-19," Finance Research Letters, Elsevier, vol. 36(C).
    4. 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.
    5. Haroon, Omair & Rizvi, Syed Aun R., 2020. "COVID-19: Media coverage and financial markets behavior—A sectoral inquiry," Journal of Behavioral and Experimental Finance, Elsevier, vol. 27(C).
    6. Bash, Ahmad & Alsaifi, Khaled, 2019. "Fear from uncertainty: An event study of Khashoggi and stock market returns," Journal of Behavioral and Experimental Finance, Elsevier, vol. 23(C), pages 54-58.
    7. Scott R. Baker & Nicholas Bloom & Steven J. Davis & Kyle J. Kost & Marco C. Sammon & Tasaneeya Viratyosin, 2020. "The Unprecedented Stock Market Impact of COVID-19," NBER Working Papers 26945, National Bureau of Economic Research, Inc.
    8. Yinghao Chen & Xiaoliang Xie & Tianle Zhang & Jiaxian Bai & Muzhou Hou, 2020. "A deep residual compensation extreme learning machine and applications," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(6), pages 986-999, September.
    9. Kowalewski, Oskar & Śpiewanowski, Piotr, 2020. "Stock market response to potash mine disasters," Journal of Commodity Markets, Elsevier, vol. 20(C).
    10. Zhang, Yue-Jun & Wang, Jing, 2015. "Exploring the WTI crude oil price bubble process using the Markov regime switching model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 421(C), pages 377-387.
    11. Narayan, Paresh Kumar, 2019. "Can stale oil price news predict stock returns?," Energy Economics, Elsevier, vol. 83(C), pages 430-444.
    12. Smales, Lee A., 2014. "News sentiment and the investor fear gauge," Finance Research Letters, Elsevier, vol. 11(2), pages 122-130.
    13. Takeda, Fumiko & Wakao, Takumi, 2014. "Google search intensity and its relationship with returns and trading volume of Japanese stocks," Pacific-Basin Finance Journal, Elsevier, vol. 27(C), pages 1-18.
    14. Salisu, Afees A. & Oloko, Tirimisiyu F., 2015. "Modeling oil price–US stock nexus: A VARMA–BEKK–AGARCH approach," Energy Economics, Elsevier, vol. 50(C), pages 1-12.
    15. Herrera, Ana María & Hu, Liang & Pastor, Daniel, 2018. "Forecasting crude oil price volatility," International Journal of Forecasting, Elsevier, vol. 34(4), pages 622-635.
    16. Lopez, Jose A, 2001. "Evaluating the Predictive Accuracy of Volatility Models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 20(2), pages 87-109, March.
    17. Bange, Mary M., 2000. "Do the Portfolios of Small Investors Reflect Positive Feedback Trading?," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 35(2), pages 239-255, June.
    18. Wei Guo & Tao Xu & Keming Tang & Jianjiang Yu & Shuangshuang Chen, 2018. "Online Sequential Extreme Learning Machine with Generalized Regularization and Adaptive Forgetting Factor for Time-Varying System Prediction," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-22, May.
    19. 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.
    20. Zhou, Yanting & Wang, Yanan & Wang, Kai & Kang, Le & Peng, Fei & Wang, Licheng & Pang, Jinbo, 2020. "Hybrid genetic algorithm method for efficient and robust evaluation of remaining useful life of supercapacitors," Applied Energy, Elsevier, vol. 260(C).
    21. Claudiu Albulescu, 2020. "Coronavirus and financial volatility: 40 days of fasting and fear," Papers 2003.04005, arXiv.org.
    22. Brad M. Barber & Terrance Odean, 2008. "All That Glitters: The Effect of Attention and News on the Buying Behavior of Individual and Institutional Investors," Review of Financial Studies, Society for Financial Studies, vol. 21(2), pages 785-818, April.
    23. Groß-Klußmann, Axel & Hautsch, Nikolaus, 2011. "When machines read the news: Using automated text analytics to quantify high frequency news-implied market reactions," Journal of Empirical Finance, Elsevier, vol. 18(2), pages 321-340, March.
    24. Cepoi, Cosmin-Octavian, 2020. "Asymmetric dependence between stock market returns and news during COVID-19 financial turmoil," Finance Research Letters, Elsevier, vol. 36(C).
    25. John R. Nofsinger & Richard W. Sias, 1999. "Herding and Feedback Trading by Institutional and Individual Investors," Journal of Finance, American Finance Association, vol. 54(6), pages 2263-2295, December.
    26. Paul C. Tetlock, 2007. "Giving Content to Investor Sentiment: The Role of Media in the Stock Market," Journal of Finance, American Finance Association, vol. 62(3), pages 1139-1168, June.
    27. Shanaev, Savva & Ghimire, Binam, 2019. "Is all politics local? Regional political risk in Russia and the panel of stock returns," Journal of Behavioral and Experimental Finance, Elsevier, vol. 21(C), pages 70-82.
    28. Harvey, David & Leybourne, Stephen & Newbold, Paul, 1997. "Testing the equality of prediction mean squared errors," International Journal of Forecasting, Elsevier, vol. 13(2), pages 281-291, June.
    29. Wang, Jue & Athanasopoulos, George & Hyndman, Rob J. & Wang, Shouyang, 2018. "Crude oil price forecasting based on internet concern using an extreme learning machine," International Journal of Forecasting, Elsevier, vol. 34(4), pages 665-677.
    Full references (including those not matched with items on IDEAS)

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