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Violation duration as a better way of VaR model evaluation : evidence from Turkish market portfolio

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  • Kilic, Ekrem

Abstract

Financial crisis those we have been experienced during last two decades encouraged the efforts of both academicians and the market participants to develop clear representations of the risk exposure of a �nancial institute. As a useful tool for measuring market risk of a portfolio, Value-at-Risk has emerged as the standard. However, there are several alternative Value-at-Risk implementations which may pro- duce signi�cantly di¤erent Value-at-Risk forecasts. Thus, evaluation of Value-at-Risk forecasts is as crucial as VaR itself. In this paper I will use the methodology which has described by Christoffersen and Pelletier[6] and I extended the methodology to create duration based analogous of unconditional coverage, conditional coverage and inde- pendence tests. I evaluated 14 Value-at-Risk implementation by using a Turkish Market portfolio which contain foreing currency, stock and bonds.

Suggested Citation

  • Kilic, Ekrem, 2006. "Violation duration as a better way of VaR model evaluation : evidence from Turkish market portfolio," MPRA Paper 5610, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:5610
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    References listed on IDEAS

    as
    1. Jose A. Lopez, 1999. "Methods for evaluating value-at-risk estimates," Economic Review, Federal Reserve Bank of San Francisco, pages 3-17.
    2. Peter Christoffersen, 2004. "Backtesting Value-at-Risk: A Duration-Based Approach," Journal of Financial Econometrics, Oxford University Press, vol. 2(1), pages 84-108.
    3. Peter Christoffersen & Jinyong Hahn & Atsushi Inoue, 1999. "Testing, Comparing, and Combining Value at Risk Measures," Center for Financial Institutions Working Papers 99-44, Wharton School Center for Financial Institutions, University of Pennsylvania.
    4. Jon Danielsson & Casper G. de Vries, 1998. "Beyond the Sample: Extreme Quantile and Probability Estimation," FMG Discussion Papers dp298, Financial Markets Group.
    5. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    6. Ekrem Kilic, 2005. "Forecasting Volatility of Turkish Markets: A Comparison of Thin and Thick Models," Econometrics 0510007, University Library of Munich, Germany.
    7. Pagan,Adrian & Ullah,Aman, 1999. "Nonparametric Econometrics," Cambridge Books, Cambridge University Press, number 9780521355643.
    8. Paul H. Kupiec, 1995. "Techniques for verifying the accuracy of risk measurement models," Finance and Economics Discussion Series 95-24, Board of Governors of the Federal Reserve System (U.S.).
    9. Christoffersen, Peter & Hahn, Jinyong & Inoue, Atsushi, 2001. "Testing and comparing Value-at-Risk measures," Journal of Empirical Finance, Elsevier, vol. 8(3), pages 325-342, July.
    10. Susan Thomas & Mandira Sarma & Ajay Shah, 2003. "Selection of Value-at-Risk models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 22(4), pages 337-358.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Value-at-Risk; model evaluation; conditional cover- age; duration based coverage testing;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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