Mapa, Dennis S. and Suaiso, Oliver Q. (2009): Measuring market risk using extreme value theory.
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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.
|Item Type:||MPRA Paper|
|Original Title:||Measuring market risk using extreme value theory|
|Keywords:||extreme value theory, peaks-over-threshold, value-at-risk, market risk, risk management|
|Subjects:||G - Financial Economics > G1 - General Financial Markets > G12 - Asset Pricing ; Trading Volume ; Bond Interest Rates
C - Mathematical and Quantitative Methods > C2 - Single Equation Models ; Single Variables > C22 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes
C - Mathematical and Quantitative Methods > C0 - General > C01 - Econometrics
|Depositing User:||Dennis S. Mapa|
|Date Deposited:||11. Mar 2010 10:32|
|Last Modified:||30. Dec 2015 18:24|
Bangko Sentral ng Pilipinas (BSP) [2006a] “Circular No. 538: Revised Risk-Based Capital Adequacy Framework”, BSP Regulations. BSP.
Bangko Sentral ng Pilipinas (BSP) [2006b] “Circular No. 544: Guidelines on Market Risk Management”, BSP Regulations. BSP.
Bangko Sentral ng Pilipinas. Website, www.bsp.gov.ph.
Bank for International Settlements. Website, www.bis.org.
Basel Committee on Banking Supervision  Supervisory framework for the use of “backtesting” in conjunction with the internal model approach to market risk capital requirements. Bank for International Settlements.
Basel Committee on Banking Supervision  International convergence of capital measurement and capital standards. Bank for International Settlements.
Bollerslev, T.  “Generalized autoregressive conditional heteroscedasticity”, Journal of Econometrics 31: 307-327.
Christoffersen, P.  “Evaluating interval forecast”, International Economic Review 3(4): 841-862.
Christoffersen, P. and D. Pelletier  “Backtesting value-at-risk: a duration-based approach”, CIRANO Scientific Series.
Danielsson, J. and C. G. de Vries  “Value-at-risk and extreme returns”, Annales D’Economie et de Statistique 60: 239-270.
Embrechts, P., S. Resnick, and G. Samorodnitsky  “Extreme value theory as a risk management tool”, North American Actuarial Journal 3: 30-41.
Engel, J. and M. Gizycki  “Conservatism, accuracy and efficiency: comparing value-at-risk models”, Australian Prudential Regulation Authority, Reserve Bank of Australia, Working Paper 2.
Engle, R. F.  “Autoregressive conditional heteroscedastic models with estimates of the variance of United Kingdom inflation”, Econometrica 50: 987-1007.
Gencay, R., F. Selcuk, and A. Ulugulyagci  “High volatility, thick tails and extreme value theory in value-at-risk estimation”, Elsevier, Insurance: Mathematics & Economics 33: 337-356.
Hendricks, D.  “Evaluation of value-at-risk models using historical data”, Federal Reserve Bank of New York, Economic Policy Review 2: 39-69.
Jorion, P.  Value at risk: the new benchmark for managing financial risk. USA: McGraw-Hill.
Lopez, J.  “Methods for evaluating value-at-risk estimates”, Federal Reserve Bank of New York, Economic Policy Review, October, Research Paper No. 9802.
McNeil, A. J.  “Calculating quantile risk measures for financial time series using extreme value theory”, Department of Mathematics, ETH, Swiss Federal Technical University, ETHE-Collection, http://e-collection.ethbib.ethz.ch/.
McNeil, A. J.  “Extreme value theory for risk managers” in: Internal modelling and CAD II. Risk Books: 93-113.
McNeil, A. J. and R. Frey  “Estimation of tail-related risk measures for heteroscedastic financial time series: an extreme value approach”, Journal of Empirical Finance 7: 271-300.
Money Market Association of the Philippines. Website, www.mart.com.ph.
Morgan J. P.  RiskMetrics technical document. Fourth edition. New York: J.P. Morgan.
Philippine Dealing & Exchange Corp. Website, www.pdex.com.ph.
Professional Risk Manager’s International Association. Website, www.prmia.org.
Tsay, R.  Analysis of financial time series. Second edition. John Wiley & Sons.