IDEAS home Printed from https://ideas.repec.org/a/eee/phsmap/v607y2022ics0378437122007701.html
   My bibliography  Save this article

Dynamic risk resonance between crude oil and stock market by econophysics and machine learning

Author

Listed:
  • Li, Jiang-Cheng
  • Xu, Ming-Zhe
  • Han, Xu
  • Tao, Chen

Abstract

Changes in crude oil prices have a strong impact on global stock markets. As the largest importer of crude oil, China’s international crude oil market and stock market have a strong risk of contagion. It is of great significance to study the stochastic resonance effect between crude oil market and Chinese stock market. This paper proposes the dynamic risk resonance analysis method based on econophysics and machine learning. From the perspective of stochastic resonance in nonequilibrium statistical physics, we set up a new periodic capital asset pricing model based on econophysics and asset pricing theory, and introduce signal power amplification to describe the risk resonance effect of crude oil price and Chinese stock market. Then, we analyze the dynamic risk resonance between crude oil and stock market impacted by systemic risk and investor heterogeneity. The results demonstrate that: (1) Based on machine learning and predictive testing, the in-sample and out-of-sample forecasting results show that the proposed new periodic model has better fitting performance than the benchmark model; (2) There are optimal system parameters and period information to minimize the risk resonance effect between two markets; (3) The existence of optimal investor heterogeneity leads to inverse resonance behavior between the two markets, which greatly reduces the contagion of market risks.

Suggested Citation

  • Li, Jiang-Cheng & Xu, Ming-Zhe & Han, Xu & Tao, Chen, 2022. "Dynamic risk resonance between crude oil and stock market by econophysics and machine learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    • Handle: RePEc:eee:phsmap:v:607:y:2022:i:c:s0378437122007701
    DOI: 10.1016/j.physa.2022.128212
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378437122007701
    Download Restriction: Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000
    File URL: https://libkey.io/10.1016/j.physa.2022.128212?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Ravi Varadhan & Christophe Roland, 2008. "Simple and Globally Convergent Methods for Accelerating the Convergence of Any EM Algorithm," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 35(2), pages 335-353, June.
    2. Asteriou, Dimitrios & Bashmakova, Yuliya, 2013. "Assessing the impact of oil returns on emerging stock markets: A panel data approach for ten Central and Eastern European Countries," Energy Economics, Elsevier, vol. 38(C), pages 204-211.
    3. D. Valenti & L. Schimansky-Geier & X. Sailer & B. Spagnolo, 2006. "Moment equations for a spatially extended system of two competing species," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 50(1), pages 199-203, March.
    4. Valenti, Davide & Spagnolo, Bernardo & Bonanno, Giovanni, 2007. "Hitting time distributions in financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(1), pages 311-320.
    5. Zhang, Xiaoyu & Liu, Yongfu, 2020. "The dynamic impact of international agricultural commodity price fluctuation on Chinese agricultural commodity prices," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 23(3), August.
    6. Rosenberg, Signe, 2019. "The effects of conventional and unconventional monetary policy on house prices in the Scandinavian countries," Journal of Housing Economics, Elsevier, vol. 46(C).
    7. G. Bonanno & D. Valenti & B. Spagnolo, 2005. "Role of Noise in a Market Model with Stochastic Volatility," Papers cond-mat/0510154, arXiv.org, revised Oct 2006.
    8. Parameswaran Gopikrishnan & Vasiliki Plerou & Luis A. Nunes Amaral & Martin Meyer & H. Eugene Stanley, 1999. "Scaling of the distribution of fluctuations of financial market indices," Papers cond-mat/9905305, arXiv.org.
    9. Vijay Victor & Dibin K K & Meenu Bhaskar & Farheen Naz, 2021. "Investigating the Dynamic Interlinkages between Exchange Rates and the NSE NIFTY Index," JRFM, MDPI, vol. 14(1), pages 1-13, January.
    10. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
    11. Meng, Juan & Nie, He & Mo, Bin & Jiang, Yonghong, 2020. "Risk spillover effects from global crude oil market to China’s commodity sectors," Energy, Elsevier, vol. 202(C).
    12. Wang, Xunxiao & Wang, Yudong, 2019. "Volatility spillovers between crude oil and Chinese sectoral equity markets: Evidence from a frequency dynamics perspective," Energy Economics, Elsevier, vol. 80(C), pages 995-1009.
    13. Ma, Feng & Wei, Yu & Huang, Dengshi & Chen, Yixiang, 2014. "Which is the better forecasting model? A comparison between HAR-RV and multifractality volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 405(C), pages 171-180.
    14. Wei, Yu & Qin, Songkun & Li, Xiafei & Zhu, Sha & Wei, Guiwu, 2019. "Oil price fluctuation, stock market and macroeconomic fundamentals: Evidence from China before and after the financial crisis," Finance Research Letters, Elsevier, vol. 30(C), pages 23-29.
    15. Kumar, Satish, 2019. "Asymmetric impact of oil prices on exchange rate and stock prices," The Quarterly Review of Economics and Finance, Elsevier, vol. 72(C), pages 41-51.
    16. Julia Kielmann & Hans Manner & Aleksey Min, 2022. "Stock market returns and oil price shocks: A CoVaR analysis based on dynamic vine copula models," Empirical Economics, Springer, vol. 62(4), pages 1543-1574, April.
    17. Nadal, Raquel & Szklo, Alexandre & Lucena, André, 2017. "Time-varying impacts of demand and supply oil shocks on correlations between crude oil prices and stock markets indices," Research in International Business and Finance, Elsevier, vol. 42(C), pages 1011-1020.
    18. Lu Yang & Lei Yang & Kung-Cheng Ho & Shigeyuki Hamori, 2019. "Determinants of the Long-Term Correlation between Crude Oil and Stock Markets," Energies, MDPI, vol. 12(21), pages 1-15, October.
    19. Bernardo Spagnolo & Davide Valenti, 2008. "Volatility Effects on the Escape Time in Financial Market Models," Papers 0810.1625, arXiv.org.
    20. G. Bonanno & D. Valenti & B. Spagnolo, 2006. "Role of noise in a market model with stochastic volatility," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 53(3), pages 405-409, October.
    21. Chaohua He & Guangchen Li & Hai Fan & Weixian Wei, 2021. "Correlation between Shanghai crude oil futures, stock, foreign exchange, and gold markets: a GARCH-vine-copula method," Applied Economics, Taylor & Francis Journals, vol. 53(11), pages 1249-1263, March.
    22. Gençay, Ramazan & Dacorogna, Michel & Muller, Ulrich A. & Pictet, Olivier & Olsen, Richard, 2001. "An Introduction to High-Frequency Finance," Elsevier Monographs, Elsevier, edition 1, number 9780122796715.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tao, Chen & Zhong, Guang-Yan & Li, Jiang-Cheng, 2023. "Dynamic correlation and risk resonance among industries of Chinese stock market: New evidence from time–frequency domain and complex network perspectives," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 614(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Leng, Na & Li, Jiang-Cheng, 2020. "Forecasting the crude oil prices based on Econophysics and Bayesian approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    2. Ko, Bonggyun & Song, Jae Wook, 2018. "A simple analytics framework for evaluating mean escape time in different term structures with stochastic volatility," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 505(C), pages 398-412.
    3. Zhou, Wei & Zhong, Guang-Yan & Li, Jiang-Cheng, 2022. "Stability of financial market driven by information delay and liquidity in delay agent-based model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 600(C).
    4. Koo, Eunho & Kim, Geonwoo, 2017. "Explicit formula for the valuation of catastrophe put option with exponential jump and default risk," Chaos, Solitons & Fractals, Elsevier, vol. 101(C), pages 1-7.
    5. Li, Jiang-Cheng & Tao, Chen & Li, Hai-Feng, 2022. "Dynamic forecasting performance and liquidity evaluation of financial market by Econophysics and Bayesian methods," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 588(C).
    6. Tzouras, Spilios & Anagnostopoulos, Christoforos & McCoy, Emma, 2015. "Financial time series modeling using the Hurst exponent," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 425(C), pages 50-68.
    7. Dong, Yang & Wen, Shu-hui & Hu, Xiao-bing & Li, Jiang-Cheng, 2020. "Stochastic resonance of drawdown risk in energy market prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 540(C).
    8. Gong, Xiao-li & Zhuang, Xin-tian, 2016. "Option pricing and hedging for optimized Lévy driven stochastic volatility models," Chaos, Solitons & Fractals, Elsevier, vol. 91(C), pages 118-127.
    9. Wang, Weiwei & Ralescu, Dan A., 2021. "Valuation of lookback option under uncertain volatility model," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    10. Zhang, Qingye & Gao, Yan, 2016. "Optimal consumption—portfolio problem with CVaR constraints," Chaos, Solitons & Fractals, Elsevier, vol. 91(C), pages 516-521.
    11. Wu, Anshun & Dong, Yang & Luo, Yuhui & Zeng, Chunhua, 2020. "Fluctuations-induced regime shifts in the Endogenous Credit system with time delay," Chaos, Solitons & Fractals, Elsevier, vol. 134(C).
    12. Xing, Dun-Zhong & Li, Hai-Feng & Li, Jiang-Cheng & Long, Chao, 2021. "Forecasting price of financial market crash via a new nonlinear potential GARCH model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 566(C).
    13. Zhong, Guang-Yan & He, Feng & Li, Jiang-Cheng & Mei, Dong-Cheng & Tang, Nian-Sheng, 2019. "Coherence resonance-like and efficiency of financial market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 534(C).
    14. Batra, Luckshay & Taneja, H.C., 2021. "Approximate-Analytical solution to the information measure’s based quanto option pricing model," Chaos, Solitons & Fractals, Elsevier, vol. 153(P1).
    15. Jin, Yanfei & Wang, Haotian & Xu, Pengfei, 2023. "Noise-induced enhancement of stability and resonance in a tri-stable system with time-delayed feedback," Chaos, Solitons & Fractals, Elsevier, vol. 168(C).
    16. Zhong, Guang-Yan & Li, Jiang-Cheng & Jiang, George J. & Li, Hai-Feng & Tao, Hui-Ming, 2018. "The time delay restraining the herd behavior with Bayesian approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 507(C), pages 335-346.
    17. Bernardo Spagnolo & Davide Valenti, 2008. "Volatility Effects on the Escape Time in Financial Market Models," Papers 0810.1625, arXiv.org.
    18. Li, Jiangcheng & Zhang, Chunmin & Liu, Jifa & Li, Zhen & Yang, Xuan, 2018. "An application of Mean Escape Time and metapopulation on forestry catastrophe insurance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 495(C), pages 312-323.
    19. Mi, Li-Na & Guo, Yong-Feng & Zhang, Meng & Zhuo, Xiao-Jing, 2023. "Stochastic resonance in gene transcriptional regulatory system driven by Gaussian noise and Lévy noise," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).
    20. Naimzada, Ahmad & Pireddu, Marina, 2015. "Real and financial interacting markets: A behavioral macro-model," Chaos, Solitons & Fractals, Elsevier, vol. 77(C), pages 111-131.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:phsmap:v:607:y:2022:i:c:s0378437122007701. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/physica-a-statistical-mechpplications/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.