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Equity Risk and Return across Hidden Market Regimes

Author

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  • Dmitry A. Endovitsky

    (Faculty of Economics, Voronezh State University, 394018 Voronezh, Russia)

  • Viacheslav V. Korotkikh

    (Faculty of Economics, Voronezh State University, 394018 Voronezh, Russia)

  • Denis A. Khripushin

    (Faculty of Economics, Voronezh State University, 394018 Voronezh, Russia)

Abstract

The key to understanding the dynamics of stock markets, particularly the mechanisms of their changes, is in the concept of the market regime. It is regarded as a regular transition from one state to another. Although the market agenda is never the same, its functioning regime allows us to reveal the logic of its development. The article employs the concept of financial turbulence to identify hidden market regimes. These are revealed through the ratio of the components, which describe single changes of correlated risks and volatility. The combinations of typical and atypical variates of correlational and magnitude components of financial turbulence allowed four hidden regimes to be revealed. These were arranged by the degree of financial turbulence, conceptually analyzed and assessed from the perspective of their duration. The empirical data demonstrated ETF day trading profits for S&P 500 sectors, covering the period of January 1998–August 2020, as well as day trade profits of the Russian blue chips within the period of October 2006–February 2021. The results show a significant difference in regard to the market performance and volatility, which depend on hidden regimes. Both sample data groups demonstrated similar contemporaneous and lagged effects, which allows the prediction of volatility jumps in the periods following atypical correlations.

Suggested Citation

  • Dmitry A. Endovitsky & Viacheslav V. Korotkikh & Denis A. Khripushin, 2021. "Equity Risk and Return across Hidden Market Regimes," Risks, MDPI, vol. 9(11), pages 1-21, October.
    • Handle: RePEc:gam:jrisks:v:9:y:2021:i:11:p:188-:d:662114
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    as
    1. Andrew Ang & Allan Timmermann, 2012. "Regime Changes and Financial Markets," Annual Review of Financial Economics, Annual Reviews, vol. 4(1), pages 313-337, October.
    2. Joanne Armstrong & David Bradfield, 2015. "Correlation Surprise: The African And South African Case," The African Finance Journal, Africagrowth Institute, vol. 17(2), pages 55-83.
    3. Bali, Turan G. & Cakici, Nusret & Whitelaw, Robert F., 2011. "Maxing out: Stocks as lotteries and the cross-section of expected returns," Journal of Financial Economics, Elsevier, vol. 99(2), pages 427-446, February.
    4. Seven, Ünal & Yılmaz, Fatih, 2021. "World equity markets and COVID-19: Immediate response and recovery prospects," Research in International Business and Finance, Elsevier, vol. 56(C).
    5. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    6. Matos, Paulo & Costa, Antonio & da Silva, Cristiano, 2021. "COVID-19, stock market and sectoral contagion in US: a time-frequency analysis," Research in International Business and Finance, Elsevier, vol. 57(C).
    7. O’Donnell, Niall & Shannon, Darren & Sheehan, Barry, 2021. "Immune or at-risk? Stock markets and the significance of the COVID-19 pandemic," Journal of Behavioral and Experimental Finance, Elsevier, vol. 30(C).
    8. 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.
    9. J. Lars Kirkby & Duy Nguyen, 2020. "Efficient Asian option pricing under regime switching jump diffusions and stochastic volatility models," Annals of Finance, Springer, vol. 16(3), pages 307-351, September.
    10. Kenneth R. Szulczyk & Changyong Zhang, 2020. "Switching-regime regression for modeling and predicting a stock market return," Empirical Economics, Springer, vol. 59(5), pages 2385-2403, November.
    11. Naidu, Dharmendra & Ranjeeni, Kumari, 2021. "Effect of coronavirus fear on the performance of Australian stock returns: Evidence from an event study," Pacific-Basin Finance Journal, Elsevier, vol. 66(C).
    12. Liu, Yue & Sun, Huaping & Zhang, Jijian & Taghizadeh-Hesary, Farhad, 2020. "Detection of volatility regime-switching for crude oil price modeling and forecasting," Resources Policy, Elsevier, vol. 69(C).
    13. Misha Beek & Michel Mandjes & Peter Spreij & Erik Winands, 2020. "Regime switching affine processes with applications to finance," Finance and Stochastics, Springer, vol. 24(2), pages 309-333, April.
    14. David G. McMillan, 2021. "Forecasting U.S. stock returns," The European Journal of Finance, Taylor & Francis Journals, vol. 27(1-2), pages 86-109, January.
    15. Dmitry ENDOVITSKY & Valery DAVNIS & Viacheslav KOROTKIKH, 2017. "On Two Hypotheses in Economic Analysis of Stochastic Processes," Journal of Advanced Research in Law and Economics, ASERS Publishing, vol. 8(8), pages 2391-2398.
    16. Paolella, Marc S. & Polak, Paweł & Walker, Patrick S., 2019. "Regime switching dynamic correlations for asymmetric and fat-tailed conditional returns," Journal of Econometrics, Elsevier, vol. 213(2), pages 493-515.
    17. José Antonio Núñez & Mario I Contreras-Valdez & Carlos A Franco-Ruiz, 2019. "Statistical analysis of bitcoin during explosive behavior periods," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-22, March.
    18. Fu, Fangjian, 2009. "Idiosyncratic risk and the cross-section of expected stock returns," Journal of Financial Economics, Elsevier, vol. 91(1), pages 24-37, January.
    19. Peter Nystrup & Henrik Madsen & Erik Lindström, 2018. "Dynamic portfolio optimization across hidden market regimes," Quantitative Finance, Taylor & Francis Journals, vol. 18(1), pages 83-95, January.
    20. Alan E. H. Speight & David G. McMillan, 2004. "Daily volatility forecasts: reassessing the performance of GARCH models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(6), pages 449-460.
    21. Jean-Pierre Fouque & Ronnie Sircar & Thaleia Zariphopoulou, 2017. "Portfolio Optimization And Stochastic Volatility Asymptotics," Mathematical Finance, Wiley Blackwell, vol. 27(3), pages 704-745, July.
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