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A singular value decomposition entropy approach to assess the impact of Covid-19 on the informational efficiency of the WTI crude oil market

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  • Espinosa-Paredes, G.
  • Rodriguez, E.
  • Alvarez-Ramirez, J.

Abstract

This work investigates the impact of the Covid-19 outbreak on crude oil market efficiency. The approach is based on the singular value decomposition (SVD) entropy. Iso-distributional surrogate data test was used to contrast the results against random patterns, and phase randomization based on Fourier transform was used to assess nonlinearities. The analysis considered the WTI market and focused on the Covid-19 pandemic period January 2020–November 2021 and contrasted with the long preceding period from January 2000 to date. It was found that the crude oil market was informationally efficient most of the time with small sporadic deviations from efficiency in the pre-Covid-19 years. The Covid-19 period exhibited the largest deviations from efficiency, mainly in the first months of the outbreak, accompanied by a marked reduction of nonlinear components. The analysis was conducted for different scales, and the results showed that the deviations from efficiency were more pronounced for quarterly scales. For the sake of comparison, the analysis was also carried out on the trading volume dynamics and the results showed that the market activity is not fully random. The dynamics of the trading volume exhibited significant deviations from the randomness behavior when the crude oil market was efficient, and a behavior that was consistent with nonlinear patterns. The opposite behavior was noted for stages when the crude oil market showed strong deviations from efficiency. Overall, the findings of this study suggest an increasing opportunity for crude oil price predictions and abnormal returns during the Covid-19 pandemic.

Suggested Citation

  • Espinosa-Paredes, G. & Rodriguez, E. & Alvarez-Ramirez, J., 2022. "A singular value decomposition entropy approach to assess the impact of Covid-19 on the informational efficiency of the WTI crude oil market," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
  • Handle: RePEc:eee:chsofr:v:160:y:2022:i:c:s0960077922004489
    DOI: 10.1016/j.chaos.2022.112238
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    as
    1. Busu, Cristian & Busu, Mihail, 2019. "Modeling the predictive power of the singular value decomposition-based entropy. Empirical evidence from the Dow Jones Global Titans 50 Index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    2. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," The Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
    3. Cheima Gharib & Salma Mefteh-Wali & Vanessa Serret & Sami Ben Jabeur, 2021. "Impact of COVID-19 pandemic on crude oil prices: Evidence from Econophysics approach," Post-Print hal-03375164, HAL.
    4. Mensi, Walid & Sensoy, Ahmet & Vo, Xuan Vinh & Kang, Sang Hoon, 2020. "Impact of COVID-19 outbreak on asymmetric multifractality of gold and oil prices," Resources Policy, Elsevier, vol. 69(C).
    5. Zhang, Bing & Li, Xiao-Ming & He, Fei, 2014. "Testing the evolution of crude oil market efficiency: Data have the conn," Energy Policy, Elsevier, vol. 68(C), pages 39-52.
    6. Okorie, David Iheke & Lin, Boqiang, 2021. "Adaptive market hypothesis: The story of the stock markets and COVID-19 pandemic," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    7. Zhang, Dayong & Hu, Min & Ji, Qiang, 2020. "Financial markets under the global pandemic of COVID-19," Finance Research Letters, Elsevier, vol. 36(C).
    8. Kristoufek, Ladislav, 2019. "Are the crude oil markets really becoming more efficient over time? Some new evidence," Energy Economics, Elsevier, vol. 82(C), pages 253-263.
    9. Saada Abba Abdullahi & Reza Kouhy & Zahid Muhammad, 2014. "Trading volume and return relationship in the crude oil futures markets," Studies in Economics and Finance, Emerald Group Publishing Limited, vol. 31(4), pages 426-438, September.
    10. Okoroafor, Ugochi Chibuzor & Leirvik, Thomas, 2022. "Time varying market efficiency in the Brent and WTI crude market," Finance Research Letters, Elsevier, vol. 45(C).
    11. Lahmiri, Salim & Bekiros, Stelios, 2020. "The impact of COVID-19 pandemic upon stability and sequential irregularity of equity and cryptocurrency markets," Chaos, Solitons & Fractals, Elsevier, vol. 138(C).
    12. Sui, Bo & Chang, Chun-Ping & Jang, Chyi-Lu & Gong, Qiang, 2021. "Analyzing causality between epidemics and oil prices: Role of the stock market," Economic Analysis and Policy, Elsevier, vol. 70(C), pages 148-158.
    13. Alvarez-Ramirez, Jose & Alvarez, Jesus & Solis, Ricardo, 2010. "Crude oil market efficiency and modeling: Insights from the multiscaling autocorrelation pattern," Energy Economics, Elsevier, vol. 32(5), pages 993-1000, September.
    14. Ortiz-Cruz, Alejandro & Rodriguez, Eduardo & Ibarra-Valdez, Carlos & Alvarez-Ramirez, Jose, 2012. "Efficiency of crude oil markets: Evidences from informational entropy analysis," Energy Policy, Elsevier, vol. 41(C), pages 365-373.
    15. Wa̧torek, Marcin & Drożdż, Stanisław & Oświȩcimka, Paweł & Stanuszek, Marek, 2019. "Multifractal cross-correlations between the world oil and other financial markets in 2012–2017," Energy Economics, Elsevier, vol. 81(C), pages 874-885.
    16. Gu, Rongbao & Shao, Yanmin, 2016. "How long the singular value decomposed entropy predicts the stock market? — Evidence from the Dow Jones Industrial Average Index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 453(C), pages 150-161.
    17. Mensi, Walid & Lee, Yun-Jung & Vinh Vo, Xuan & Yoon, Seong-Min, 2021. "Does oil price variability affect the long memory and weak form efficiency of stock markets in top oil producers and oil Consumers? Evidence from an asymmetric MF-DFA approach," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    18. Mnif, Emna & Jarboui, Anis & Mouakhar, Khaireddine, 2020. "How the cryptocurrency market has performed during COVID 19? A multifractal analysis," Finance Research Letters, Elsevier, vol. 36(C).
    19. Marcin Wk{a}torek & Stanis{l}aw Dro.zd.z & Pawe{l} O'swic{e}cimka & Marek Stanuszek, 2018. "Multifractal cross-correlations between the World Oil and other Financial Markets in 2012-2017," Papers 1812.08548, arXiv.org, revised Jun 2019.
    20. He, Ling-Yun & Chen, Shu-Peng, 2010. "Are crude oil markets multifractal? Evidence from MF-DFA and MF-SSA perspectives," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(16), pages 3218-3229.
    21. Wang, Jian & Shao, Wei & Kim, Junseok, 2020. "Analysis of the impact of COVID-19 on the correlations between crude oil and agricultural futures," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    22. Gozbasi, Onur & Kucukkaplan, Ilhan & Nazlioglu, Saban, 2014. "Re-examining the Turkish stock market efficiency: Evidence from nonlinear unit root tests," Economic Modelling, Elsevier, vol. 38(C), pages 381-384.
    23. Wang, Yudong & Liu, Li, 2010. "Is WTI crude oil market becoming weakly efficient over time?: New evidence from multiscale analysis based on detrended fluctuation analysis," Energy Economics, Elsevier, vol. 32(5), pages 987-992, September.
    24. 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).
    25. Choi, Sun-Yong, 2021. "Analysis of stock market efficiency during crisis periods in the US stock market: Differences between the global financial crisis and COVID-19 pandemic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 574(C).
    26. Gharib, Cheima & Mefteh-Wali, Salma & Serret, Vanessa & Ben Jabeur, Sami, 2021. "Impact of COVID-19 pandemic on crude oil prices: Evidence from Econophysics approach," Resources Policy, Elsevier, vol. 74(C).
    27. Tabak, Benjamin M. & Cajueiro, Daniel O., 2007. "Are the crude oil markets becoming weakly efficient over time? A test for time-varying long-range dependence in prices and volatility," Energy Economics, Elsevier, vol. 29(1), pages 28-36, January.
    28. Assaf, Ata & Bhandari, Avishek & Charif, Husni & Demir, Ender, 2022. "Multivariate long memory structure in the cryptocurrency market: The impact of COVID-19," International Review of Financial Analysis, Elsevier, vol. 82(C).
    29. Arouxet, M. Belén & Bariviera, Aurelio F. & Pastor, Verónica E. & Vampa, Victoria, 2022. "Covid-19 impact on cryptocurrencies: Evidence from a wavelet-based Hurst exponent," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 596(C).
    30. Caraiani, Petre, 2014. "The predictive power of singular value decomposition entropy for stock market dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 393(C), pages 571-578.
    31. Spyros Spyrou, 2013. "Herding in financial markets: a review of the literature," Review of Behavioral Finance, Emerald Group Publishing Limited, vol. 5(2), pages 175-194, November.
    32. Gu, Rongbao & Xiong, Wei & Li, Xinjie, 2015. "Does the singular value decomposition entropy have predictive power for stock market? — Evidence from the Shenzhen stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 439(C), pages 103-113.
    33. Le, Thai-Ha & Le, Anh Tu & Le, Ha-Chi, 2021. "The historic oil price fluctuation during the Covid-19 pandemic: What are the causes?," Research in International Business and Finance, Elsevier, vol. 58(C).
    34. Ghazani, Majid Mirzaee & Ebrahimi, Seyed Babak, 2019. "Testing the adaptive market hypothesis as an evolutionary perspective on market efficiency: Evidence from the crude oil prices," Finance Research Letters, Elsevier, vol. 30(C), pages 60-68.
    35. Li, Yanshuang & Zhuang, Xintian & Wang, Jian & Dong, Zibing, 2021. "Analysis of the impact of COVID-19 pandemic on G20 stock markets," The North American Journal of Economics and Finance, Elsevier, vol. 58(C).
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    2. Xavier Brouty & Matthieu Garcin, 2022. "A statistical test of market efficiency based on information theory," Working Papers hal-03760478, HAL.
    3. Xavier Brouty & Matthieu Garcin, 2022. "A statistical test of market efficiency based on information theory," Papers 2208.11976, arXiv.org.

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