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Value at Risk and Market Crashes

Author

Listed:
  • 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.

Suggested Citation

  • Chris Brooks & Gita Persand, 2000. "Value at Risk and Market Crashes," ICMA Centre Discussion Papers in Finance icma-dp2000-01, Henley Business School, University of Reading.
    • Handle: RePEc:rdg:icmadp:icma-dp2000-01
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    File URL: http://www.icmacentre.ac.uk/pdf/discussion/DP2000-01.pdf
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    Citations

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    Cited by:

    1. Pierre Giot & Sébastien Laurent, 2003. "Value-at-risk for long and short trading positions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(6), pages 641-663.
    2. Wolfgang Aussenegg & Tatiana Miazhynskaia, 2006. "Uncertainty in Value-at-risk Estimates under Parametric and Non-parametric Modeling," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 20(3), pages 243-264, September.
    3. 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.
    4. Chiuling Lu & Sheng‐Ching Wu & Lan‐Chih Ho, 2009. "Applying VaR to REITs: A comparison of alternative methods," Review of Financial Economics, John Wiley & Sons, vol. 18(2), pages 97-102, April.
    5. Radu Tunaru, 2015. "Model Risk in Financial Markets:From Financial Engineering to Risk Management," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 9524, December.
    6. Silvia Stanescu & Radu Tunaru, 2013. "Quantifying the uncertainty in VaR and expected shortfall estimates," Chapters, in: Adrian R. Bell & Chris Brooks & Marcel Prokopczuk (ed.), Handbook of Research Methods and Applications in Empirical Finance, chapter 15, pages 357-372, Edward Elgar Publishing.
    7. Toktam Valizadeh & Saeid Rezakhah & Ferdous Mohammadi Basatini, 2021. "On time‐varying amplitude HGARCH model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(2), pages 2538-2547, April.
    8. Gita Persand & Chris Brooks, 2003. "Volatility forecasting for risk management," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(1), pages 1-22.

    More about this item

    Keywords

    Internal Risk Management Models; Stock Market Volatility; Value at Risk Models; Extreme Market Movements; Correlation Matrices; Mulivariate ARCH Model;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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