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Value at Risk and Market Crashes

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Author Info

  • Chris Brooks

    ()
    (ICMA Centre, University of Reading)

  • Gita Persand

    ()
    (ICMA Centre, University of Reading)

Abstract

Many popular techniques for determining a securities firm’s value at risk are based upon the calculation of the historical volatility of returns to the assets that comprise the portfolio, and of the correlations between them. One such approach is the J.P. Morgan RiskMetrics methodology using Markowitz portfolio theory. An implicit assumption underlying this methodology is that the volatilities and correlations are constant throughout the sample period, and in particular that they are not systematically related to one another. However, it has been suggested in a number of studies that the correlation between markets increases when the individual volatilities are high. This paper demonstrates that this type of relationship between correlation and volatility can lead to a downward bias in the estimated value at risk, and proposes a number of pragmatic approaches that risk managers might adopt for dealing with this issue.

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File URL: http://www.icmacentre.ac.uk/pdf/discussion/DP2000-01.pdf
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Bibliographic Info

Paper provided by Henley Business School, Reading University in its series ICMA Centre Discussion Papers in Finance with number icma-dp2000-01.

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Length: 30 pages
Date of creation: 2000
Date of revision:
Publication status: Published in Financial Analysts Journal 2002, 58:5, 87-97.
Handle: RePEc:rdg:icmadp:icma-dp2000-01

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Web page: http://www.henley.reading.ac.uk/
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Related research

Keywords: Internal Risk Management Models; Stock Market Volatility; Value at Risk Models; Extreme Market Movements; Correlation Matrices; Mulivariate ARCH Model;

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Cited by:
  1. GIOT, Pierre & LAURENT, Sébastien, 2001. "Value-at-risk for long and short trading positions," CORE Discussion Papers 2001022, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  2. Lu, Chiuling & Wu, Sheng-Ching & Ho, Lan-Chih, 2009. "Applying VaR to REITs: A comparison of alternative methods," Review of Financial Economics, Elsevier, vol. 18(2), pages 97-102, April.
  3. Wolfgang Aussenegg & Tatiana Miazhynskaia, 2006. "Uncertainty in Value-at-risk Estimates under Parametric and Non-parametric Modeling," Financial Markets and Portfolio Management, Springer, vol. 20(3), pages 243-264, September.
  4. Gita Persand & Chris Brooks, 2003. "Volatility forecasting for risk management," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(1), pages 1-22.

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