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Estimating Systematic Risk Using Time Varying Distributions

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  • Gregory Koutmos
  • Johan Knif

Abstract

This article proposes a dynamic vector GARCH model for the estimation of time‐varying betas. The model allows the conditional variances and the conditional covariance between individual portfolio returns and market portfolio returns to respond asymmetrically to past innovations depending on their sign. Covariances tend to be higher during market declines. There is substantial time variation in betas but the evidence on beta asymmetry is mixed. Specifically, in 50% of the cases betas are higher during market declines and for the remaining 50% the opposite is true. A time series analysis of estimated time varying betas reveals that they follow stationary mean‐reverting processes. The average degree of persistence is approximately four days. It is also found that the static market model overstates non‐market or, unsystematic risk by more than 10%. On the basis of an array of diagnostics it is confirmed that the vector GARCH model provides a richer framework for the analysis of the dynamics of systematic risk.

Suggested Citation

  • Gregory Koutmos & Johan Knif, 2002. "Estimating Systematic Risk Using Time Varying Distributions," European Financial Management, European Financial Management Association, vol. 8(1), pages 59-73, March.
    • Handle: RePEc:bla:eufman:v:8:y:2002:i:1:p:59-73
    DOI: 10.1111/1468-036X.00176
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    Cited by:

    1. Hooy Chee-Wooi & Robert D. Brooks, 2015. "The Components of Systematic Risk and Their Determinants in The Malaysian Equity Market," Asian Academy of Management Journal of Accounting and Finance (AAMJAF), Penerbit Universiti Sains Malaysia, vol. 11(2), pages 151-176.
    2. Luc Savard, 2010. "Scaling up infrastructure spending in the Philippines: A CGE top-down bottom-up microsimulation approach," International Journal of Microsimulation, International Microsimulation Association, vol. 3(1), pages 43-59.
    3. Koutmos, Gregory & Martin, Anna D., 2007. "Modeling time variation and asymmetry in foreign exchange exposure," Journal of Multinational Financial Management, Elsevier, vol. 17(1), pages 61-74, February.
    4. Baule, Rainer & Korn, Olaf & Saßning, Sven, 2013. "Which beta is best? On the information content of option-implied betas," CFR Working Papers 13-11, University of Cologne, Centre for Financial Research (CFR).
    5. Aysun, Uluc & Guldi, Melanie, 2011. "Exchange rate exposure: A nonparametric approach," Emerging Markets Review, Elsevier, vol. 12(4), pages 321-337.
    6. Kai-Li Wang & Mei-Ling Chen, 2007. "The dynamics in the spot, futures, and call options with basis asymmetries: an intraday analysis in a generalized multivariate GARCH-M MSKST framework," Review of Quantitative Finance and Accounting, Springer, vol. 29(4), pages 371-394, November.
    7. Fernandez, Pablo, 2004. "Are calculated betas good for anything?," IESE Research Papers D/555, IESE Business School.
    8. Wang, Kai-Li & Fawson, Christopher & Chen, Mei-Ling & Wu, An-Chi, 2014. "Characterizing information flows among spot, deliverable forward and non-deliverable forward exchange rate markets: A cross-country comparison," Pacific-Basin Finance Journal, Elsevier, vol. 27(C), pages 115-137.
    9. Manuel Ammann & Michael Verhofen, 2008. "Testing Conditional Asset Pricing Models Using a Markov Chain Monte Carlo Approach," European Financial Management, European Financial Management Association, vol. 14(3), pages 391-418, June.
    10. Hayette Gatfaoui, 2010. "Capital Asset Pricing Model," Post-Print hal-00589904, HAL.
    11. Gregory Koutmos & Andreas Pericli & Lenos Trigeorgis, 2006. "Short-term Dynamics in the Cyprus Stock Exchange," The European Journal of Finance, Taylor & Francis Journals, vol. 12(3), pages 205-216.
    12. Rainer Baule & Olaf Korn & Sven Saßning, 2016. "Which Beta Is Best? On the Information Content of Option†implied Betas," European Financial Management, European Financial Management Association, vol. 22(3), pages 450-483, June.
    13. Johan Knif & James W. Kolari & Gregory Koutmos & Seppo Pynonen, 2023. "Modeling the Time Variation in Factor Exposures," Journal of Finance and Investment Analysis, SCIENPRESS Ltd, vol. 12(2), pages 1-2.
    14. Johann Pfitzinger, 2021. "An Interpretable Neural Network for Parameter Inference," Papers 2106.05536, arXiv.org.

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