IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v16y2023i2p67-d1045068.html
   My bibliography  Save this article

Markov-Regime Switches in Oil Markets: The Fear Factor Dynamics

Author

Listed:
  • Hiroyuki Okawa

    (Graduate School of Economics, Kobe University, Kobe 657-8501, Japan)

Abstract

This paper is an attempt to examine regime switches in the empirical relation between return dynamics and implied volatility in energy markets. The time-varying properties of the return-generating process are defined as a function of several risk factors, including oil market volatility and changes in stock prices and currency rates. The empirical evidence is based on Markov-regime switching models, which have the capacity to capture, in particular, the stochastic behavior of the OVX oil volatility index as a benchmark for investors’ fear. The results suggest that the dynamics of oil market returns are governed by two distinct regimes, a state driven by a negative relationship between returns and implied volatility and another state characterized by a more pronounced negative correlation. It is the latter regime with a stronger correlation that tends to prevail over the sample period from 2008 to 2021, but the frequency of regime shifts also seems to increase under more volatile oil price dynamics in association with significant events such as the COVID-19 pandemic. Thus, the evidence of a negative correlation structure is found to be robust to changes in the estimation period, which suggests that the oil volatility index remains a reliable gauge of market sentiment in the energy markets.

Suggested Citation

  • Hiroyuki Okawa, 2023. "Markov-Regime Switches in Oil Markets: The Fear Factor Dynamics," JRFM, MDPI, vol. 16(2), pages 1-20, January.
  • Handle: RePEc:gam:jjrfmx:v:16:y:2023:i:2:p:67-:d:1045068
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/16/2/67/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1911-8074/16/2/67/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Jeff Fleming & Barbara Ostdiek & Robert E. Whaley, 1995. "Predicting stock market volatility: A new measure," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 15(3), pages 265-302, May.
    2. Smales, Lee A., 2022. "Spreading the fear: The central role of CBOE VIX in global stock market uncertainty," Global Finance Journal, Elsevier, vol. 51(C).
    3. Brice V. Dupoyet & Corey A. Shank, 2018. "Oil prices implied volatility or direction: Which matters more to financial markets?," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 32(3), pages 275-295, August.
    4. Cheung, C. Sherman & Miu, Peter, 2010. "Diversification benefits of commodity futures," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 20(5), pages 451-474, December.
    5. repec:dau:papers:123456789/14980 is not listed on IDEAS
    6. Mensi, Walid & Beljid, Makram & Boubaker, Adel & Managi, Shunsuke, 2013. "Correlations and volatility spillovers across commodity and stock markets: Linking energies, food, and gold," Economic Modelling, Elsevier, vol. 32(C), pages 15-22.
    7. Aboura, Sofiane & Chevallier, Julien, 2013. "Leverage vs. feedback: Which Effect drives the oil market?," Finance Research Letters, Elsevier, vol. 10(3), pages 131-141.
    8. Christiane Baumeister & Lutz Kilian, 2015. "Forecasting the Real Price of Oil in a Changing World: A Forecast Combination Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 338-351, July.
    9. Connolly, Robert & Stivers, Chris & Sun, Licheng, 2005. "Stock Market Uncertainty and the Stock-Bond Return Relation," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 40(1), pages 161-194, March.
    10. M. Hashem Pesaran & Yongcheol Shin & Richard J. Smith, 2001. "Bounds testing approaches to the analysis of level relationships," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(3), pages 289-326.
    11. James D. Hamilton, 2009. "Understanding Crude Oil Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 179-206.
    12. Creti, Anna & Joëts, Marc & Mignon, Valérie, 2013. "On the links between stock and commodity markets' volatility," Energy Economics, Elsevier, vol. 37(C), pages 16-28.
    13. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    14. Liu, Bing-Yue & Ji, Qiang & Fan, Ying, 2017. "Dynamic return-volatility dependence and risk measure of CoVaR in the oil market: A time-varying mixed copula model," Energy Economics, Elsevier, vol. 68(C), pages 53-65.
    15. 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.
    16. Choi, Kyongwook & Hammoudeh, Shawkat, 2010. "Volatility behavior of oil, industrial commodity and stock markets in a regime-switching environment," Energy Policy, Elsevier, vol. 38(8), pages 4388-4399, August.
    17. repec:dau:papers:123456789/9860 is not listed on IDEAS
    18. Domenico Ferraro & Kenneth S. Rogoff & Barbara Rossi, 2011. "Can oil prices forecast exchange rates?," Working Papers 11-34, Federal Reserve Bank of Philadelphia.
    19. Demian Pouzo & Zacharias Psaradakis & Martin Sola, 2022. "Maximum Likelihood Estimation in Markov Regime‐Switching Models With Covariate‐Dependent Transition Probabilities," Econometrica, Econometric Society, vol. 90(4), pages 1681-1710, July.
    20. Mencía, Javier & Sentana, Enrique, 2013. "Valuation of VIX derivatives," Journal of Financial Economics, Elsevier, vol. 108(2), pages 367-391.
    21. Silvennoinen, Annastiina & Thorp, Susan, 2013. "Financialization, crisis and commodity correlation dynamics," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 24(C), pages 42-65.
    22. Ferraro, Domenico & Rogoff, Kenneth & Rossi, Barbara, 2015. "Can oil prices forecast exchange rates? An empirical analysis of the relationship between commodity prices and exchange rates," Journal of International Money and Finance, Elsevier, vol. 54(C), pages 116-141.
    23. MacKinnon, James G, 1996. "Numerical Distribution Functions for Unit Root and Cointegration Tests," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 11(6), pages 601-618, Nov.-Dec..
    24. Harris, Glen R., 1999. "Markov Chain Monte Carlo Estimation of Regime Switching Vector Autoregressions," ASTIN Bulletin, Cambridge University Press, vol. 29(1), pages 47-79, May.
    25. Lombardi, Marco J. & Ravazzolo, Francesco, 2016. "On the correlation between commodity and equity returns: Implications for portfolio allocation," Journal of Commodity Markets, Elsevier, vol. 2(1), pages 45-57.
    26. Robert S. Pindyck, 2001. "The Dynamics of Commodity Spot and Futures Markets: A Primer," The Energy Journal, , vol. 22(3), pages 1-29, July.
    27. Itay Goldstein & Liyan Yang, 2022. "Commodity Financialization and Information Transmission," Journal of Finance, American Finance Association, vol. 77(5), pages 2613-2667, October.
    28. Kazuhiko NISHINA & Tatsuro Nabil MAGHREBI & Moo-Sung KIM, 2006. "Stock Market Volatility And The Forecasting Accuracy Of Implied Volatility Indices," Discussion Papers in Economics and Business 06-09, Osaka University, Graduate School of Economics.
    29. Gerald R. Jensen & Robert R. Johnson & Jeffrey M. Mercer, 2000. "Efficient use of commodity futures in diversified portfolios," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 20(5), pages 489-506, May.
    30. M. Fukasawa & I. Ishida & N. Maghrebi & K. Oya & M. Ubukata & K. Yamazaki, 2011. "Model-Free Implied Volatility: From Surface To Index," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 14(04), pages 433-463.
    31. Nabil Maghrebi & Mark J. Holmes & Kosuke Oya, 2014. "Financial instability and the short-term dynamics of volatility expectations," Applied Financial Economics, Taylor & Francis Journals, vol. 24(6), pages 377-395, March.
    32. Suyi Kim & So-Yeun Kim & Kyungmee Choi, 2019. "Analyzing Oil Price Shocks and Exchange Rates Movements in Korea using Markov Regime-Switching Models," Energies, MDPI, vol. 12(23), pages 1-16, December.
    33. Just, Małgorzata & Echaust, Krzysztof, 2020. "Stock market returns, volatility, correlation and liquidity during the COVID-19 crisis: Evidence from the Markov switching approach," Finance Research Letters, Elsevier, vol. 37(C).
    34. Kim, Chang-Jin & Piger, Jeremy & Startz, Richard, 2008. "Estimation of Markov regime-switching regression models with endogenous switching," Journal of Econometrics, Elsevier, vol. 143(2), pages 263-273, April.
    35. GIOT, Pierre, 2003. "The Asian financial crisis : the start of a regime switch in volatility," LIDAM Discussion Papers CORE 2003078, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    36. Beckmann, Joscha & Czudaj, Robert L. & Arora, Vipin, 2020. "The relationship between oil prices and exchange rates: Revisiting theory and evidence," Energy Economics, Elsevier, vol. 88(C).
    37. Sarwar, Ghulam, 2012. "Is VIX an investor fear gauge in BRIC equity markets?," Journal of Multinational Financial Management, Elsevier, vol. 22(3), pages 55-65.
    38. Gong, Xu & Xu, Jun, 2022. "Geopolitical risk and dynamic connectedness between commodity markets," Energy Economics, Elsevier, vol. 110(C).
    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. T. Sathiyaraj & T. Ambika & Ong Seng Huat, 2023. "Exponential Stability of Fractional Large-Scale Neutral Stochastic Delay Systems with Fractional Brownian Motion," JRFM, MDPI, vol. 16(5), pages 1-15, May.
    2. Mehrdoust, Farshid & Noorani, Idin & Kanniainen, Juho, 2024. "Valuation of option price in commodity markets described by a Markov-switching model: A case study of WTI crude oil market," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 215(C), pages 228-269.

    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. Ahmed, Abdullahi D. & Huo, Rui, 2021. "Volatility transmissions across international oil market, commodity futures and stock markets: Empirical evidence from China," Energy Economics, Elsevier, vol. 93(C).
    2. Gagnon, Marie-Hélène & Manseau, Guillaume & Power, Gabriel J., 2020. "They're back! Post-financialization diversification benefits of commodities," International Review of Financial Analysis, Elsevier, vol. 71(C).
    3. Sercan Demiralay & Selcuk Bayraci & H. Gaye Gencer, 2019. "Time-varying diversification benefits of commodity futures," Empirical Economics, Springer, vol. 56(6), pages 1823-1853, June.
    4. Davide, Marinella & Vesco, Paola, 2016. "Alternative Approaches for Rating INDCs: a Comparative Analysis," MITP: Mitigation, Innovation and Transformation Pathways 232716, Fondazione Eni Enrico Mattei (FEEM).
    5. Wen, Fenghua & Cao, Jiahui & Liu, Zhen & Wang, Xiong, 2021. "Dynamic volatility spillovers and investment strategies between the Chinese stock market and commodity markets," International Review of Financial Analysis, Elsevier, vol. 76(C).
    6. Stavros Degiannakis & George Filis & Vipin Arora, 2018. "Oil Prices and Stock Markets: A Review of the Theory and Empirical Evidence," The Energy Journal, , vol. 39(5), pages 85-130, September.
    7. Olson, Eric & J. Vivian, Andrew & Wohar, Mark E., 2014. "The relationship between energy and equity markets: Evidence from volatility impulse response functions," Energy Economics, Elsevier, vol. 43(C), pages 297-305.
    8. repec:ipg:wpaper:2014-561 is not listed on IDEAS
    9. Hammoudeh, Shawkat & Nguyen, Duc Khuong & Reboredo, Juan Carlos & Wen, Xiaoqian, 2014. "Dependence of stock and commodity futures markets in China: Implications for portfolio investment," Emerging Markets Review, Elsevier, vol. 21(C), pages 183-200.
    10. Shelly Singhal & Pratap Chandra Biswal, 2021. "Dynamic Commodity Portfolio Management: A Regime-switching VAR Model," Global Business Review, International Management Institute, vol. 22(2), pages 532-549, April.
    11. Liao, Jia & Qian, Qi & Xu, Xiangyun, 2018. "Whether the fluctuation of China’s financial markets have impact on global commodity prices?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 503(C), pages 1030-1040.
    12. Charfeddine, Lanouar & Benlagha, Noureddine, 2016. "A time-varying copula approach for modelling dependency: New evidence from commodity and stock markets," Journal of Multinational Financial Management, Elsevier, vol. 37, pages 168-189.
    13. Behmiri, Niaz Bashiri & Manera, Matteo & Nicolini, Marcella, 2016. "Understanding Dynamic Conditional Correlations between Commodities Futures Markets," ESP: Energy Scenarios and Policy 232223, Fondazione Eni Enrico Mattei (FEEM).
    14. Sang Hoon Kang & Ron McIver & Seong-Min Yoon, 2016. "Modeling Time-Varying Correlations in Volatility Between BRICS and Commodity Markets," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 52(7), pages 1698-1723, July.
    15. Olivier Rousse & Benoît Sévi, 2017. "Informed Trading in Oil-Futures Market," Working Papers hal-01460186, HAL.
    16. Olivier Rousse & Benoît Sévi, 2016. "Informed Trading in Oil-Futures Market," Working Papers hal-01410093, HAL.
    17. Degiannakis, Stavros & Filis, George, 2017. "Forecasting oil price realized volatility using information channels from other asset classes," Journal of International Money and Finance, Elsevier, vol. 76(C), pages 28-49.
    18. Mofleh Alshogeathri & Jamel Jouini, 2017. "Linkages Between Equity and Global Food Markets: New Evidence from Including Structural Changes," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 67(3), pages 166-198, June.
    19. Duc Khuong Nguyen & Nikolas Topaloglou & Thomas Walther, 2020. "Asset Classes and Portfolio Diversification: Evidence from a Stochastic Spanning Approach," Working Papers 2020-009, Department of Research, Ipag Business School.
    20. Sadorsky, Perry, 2014. "Modeling volatility and correlations between emerging market stock prices and the prices of copper, oil and wheat," Energy Economics, Elsevier, vol. 43(C), pages 72-81.
    21. Dimitrios Kartsonakis-Mademlis & Nikolaos Dritsakis, 2020. "Does the Choice of the Multivariate GARCH Model on Volatility Spillovers Matter? Evidence from Oil Prices and Stock Markets in G7 Countries," International Journal of Energy Economics and Policy, Econjournals, vol. 10(5), pages 164-182.

    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:gam:jjrfmx:v:16:y:2023:i:2:p:67-:d:1045068. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.