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Can switching between risk measures lead to better portfolio optimization?

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  • Brianna Cain
  • Ralf Zurbruegg

    (Business School, University of Adelaide)

Abstract

This article proposes a technique that involves switching between risk measures in different market environments, to capture the well-documented dynamic nature of risk within a portfolio optimization setting. In-sample results show categorically that switching between various measures, such as CVaR, time-varying (GARCH) variances and simple standard deviations, can lead to a better performance than using any single measure. Using a logistic probability model to determine when to switch between alternatives, out-of -sample results also show positive results. Given that this study only applies a basic switching system, it lends itself to easy application by practitioners through its simplicity, intuitive appeal and computational feasibility.

Suggested Citation

  • Brianna Cain & Ralf Zurbruegg, 2010. "Can switching between risk measures lead to better portfolio optimization?," Journal of Asset Management, Palgrave Macmillan, vol. 10(6), pages 358-369, February.
  • Handle: RePEc:pal:assmgt:v:10:y:2010:i:6:d:10.1057_jam.2009.20
    DOI: 10.1057/jam.2009.20
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    1. Alexander, Gordon J. & Baptista, Alexandre M., 2002. "Economic implications of using a mean-VaR model for portfolio selection: A comparison with mean-variance analysis," Journal of Economic Dynamics and Control, Elsevier, vol. 26(7-8), pages 1159-1193, July.
    2. Russell Davidson & Jean-Yves Duclos, 2000. "Statistical Inference for Stochastic Dominance and for the Measurement of Poverty and Inequality," Econometrica, Econometric Society, vol. 68(6), pages 1435-1464, November.
    3. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    4. Kaplanski, Guy & Kroll, Yoram, 2002. "VaR Risk Measures versus Traditional Risk Measures: an Analysis and Survey," MPRA Paper 80070, University Library of Munich, Germany.
    5. Basak, Suleyman & Shapiro, Alexander, 2001. "Value-at-Risk-Based Risk Management: Optimal Policies and Asset Prices," The Review of Financial Studies, Society for Financial Studies, vol. 14(2), pages 371-405.
    6. Acerbi, Carlo & Tasche, Dirk, 2002. "On the coherence of expected shortfall," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1487-1503, July.
    7. 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.
    8. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    9. Eling, Martin & Schuhmacher, Frank, 2007. "Does the choice of performance measure influence the evaluation of hedge funds?," Journal of Banking & Finance, Elsevier, vol. 31(9), pages 2632-2647, September.
    10. David Moreno & Paulina Marco & Ignacio Olmeda, 2005. "Risk forecasting models and optimal portfolio selection," Applied Economics, Taylor & Francis Journals, vol. 37(11), pages 1267-1281.
    11. Vikas Agarwal, 2004. "Risks and Portfolio Decisions Involving Hedge Funds," The Review of Financial Studies, Society for Financial Studies, vol. 17(1), pages 63-98.
    12. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    13. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
    14. Lan-chih Ho & John Cadle & Michael Theobald, 2008. "Portfolio selection in an expected shortfall framework during the recent ‘credit crunch’ period," Journal of Asset Management, Palgrave Macmillan, vol. 9(2), pages 121-137, July.
    15. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    16. Bera, Anil K & Higgins, Matthew L, 1993. "ARCH Models: Properties, Estimation and Testing," Journal of Economic Surveys, Wiley Blackwell, vol. 7(4), pages 305-366, December.
    17. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78, World Scientific Publishing Co. Pte. Ltd..
    18. Rockafellar, R. Tyrrell & Uryasev, Stanislav, 2002. "Conditional value-at-risk for general loss distributions," Journal of Banking & Finance, Elsevier, vol. 26(7), pages 1443-1471, July.
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    Cited by:

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