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Measuring market risk using extreme value theory

Author

Listed:
  • Jose Oliver Q. Suaiso

    (University of the Philippines School of Statistics)

  • Dennis S. Mapa

    (University of the Philippines School of Statistics)

Abstract

The adoption of Basel II standards by the Bangko Sentral ng Pilipinas initiates financial institutions to develop value-at-risk (VaR)models to measure market risk. In this paper, two VaR models are considered using the peaks-over-threshold (POT) approach of the extreme value theory (EVT) : (1) static EVT model, which is the straightforward application of pot to the bond benchmark rates; and (2) dynamic evt model, which applies pot to the residuals of the fitted AR-GARCH model. The results are compared with traditional VaR methods such as RiskMetrics and AR-GARCH-type models. The relative size, accuracy, and efficiency of the models are assessed using mean relative bias, backtesting, likelihood ratio tests, loss function, mean relative scaled bias, and computation of market risk charge. Findings show that the dynamic EVT model can capture market risk conservatively, accurately, and efficiently. It is also practical to use because it has the potential to lower a bank’s capital requirements. Comparing the two EVT models, the dynamic model is better than static as the former can address some issues in risk measurement and effectively capture market risks.

Suggested Citation

  • Jose Oliver Q. Suaiso & Dennis S. Mapa, 2009. "Measuring market risk using extreme value theory," Philippine Review of Economics, University of the Philippines School of Economics and Philippine Economic Society, vol. 46(2), pages 91-121, December.
    • Handle: RePEc:phs:prejrn:v:46:y:2009:i:2:p:91-121
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    File URL: http://pre.econ.upd.edu.ph/index.php/pre/article/view/5/670
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    References listed on IDEAS

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    1. McNeil, Alexander J. & Frey, Rudiger, 2000. "Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach," Journal of Empirical Finance, Elsevier, vol. 7(3-4), pages 271-300, November.
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    7. Gencay, Ramazan & Selcuk, Faruk & Ulugulyagci, Abdurrahman, 2003. "High volatility, thick tails and extreme value theory in value-at-risk estimation," Insurance: Mathematics and Economics, Elsevier, vol. 33(2), pages 337-356, October.
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    Cited by:

    1. Cayton, Peter Julian A. & Mapa, Dennis S., 2012. "Time-varying conditional Johnson SU density in value-at-risk (VaR) methodology," MPRA Paper 36206, University Library of Munich, Germany.
    2. Peter Julian A Cayton & Dennis S Mapa & Mary Therese A Lising, 2010. "Estimating Value At Risk Var Using Tivex Pot Models," Journal of Advanced Studies in Finance, ASERS Publishing, vol. 1(2), pages 152-170.
    3. Edward P. Santos & Dennis S. Mapa & Eloisa T. Glindro, 2010. "Estimating inflation-at-risk (IaR) using extreme value theory (EVT)," Philippine Review of Economics, University of the Philippines School of Economics and Philippine Economic Society, vol. 47(2), pages 21-40, December.

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    More about this item

    Keywords

    extreme value theory; peaks-over-threshold; value-at-risk; market risk; risk management;
    All these keywords.

    JEL classification:

    • C01 - Mathematical and Quantitative Methods - - General - - - Econometrics
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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