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Measuring market risk using extreme value theory

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  • Mapa, Dennis S.
  • Suaiso, Oliver Q.

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: (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.

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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 21246.

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Date of creation: Dec 2009
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Handle: RePEc:pra:mprapa:21246

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Keywords: extreme value theory; peaks-over-threshold; value-at-risk; market risk; risk management;

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  1. Darryll Hendricks, 1996. "Evaluation of value-at-risk models using historical data," Economic Policy Review, Federal Reserve Bank of New York, issue Apr, pages 39-69.
  2. Jose Lopez, 1998. "Methods for evaluating value-at-risk estimates," Research Paper 9802, Federal Reserve Bank of New York.
  3. 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.
  4. Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-62, November.
  5. Peter Christoffersen, 2004. "Backtesting Value-at-Risk: A Duration-Based Approach," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 2(1), pages 84-108.
  6. 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.
  7. Jon DANIELSSON & Casper G. DE VRIES, 2000. "Value-at-Risk and Extreme Returns," Annales d'Economie et de Statistique, ENSAE, issue 60, pages 239-270.
  8. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
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Cited by:
  1. Peter Julian A. Cayton & Dennis S. Mapa, Ph. D. & Mary Therese A. Lising, 2010. "Estimating Value-At-Risk (Var) Using TIVEX-POT Models," Journal of Advanced Studies in Finance, ASERS Publishing, vol. 0(2), pages 152 - 170, December.
  2. 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.

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