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Evaluating Value-at-Risk forecasts: A new set of multivariate backtests

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  • Wied, Dominik
  • Weiß, Gregor N.F.
  • Ziggel, Daniel

Abstract

We propose two new tests for detecting clustering in multivariate Value-at-Risk (VaR) forecasts. First, we consider CUSUM-tests to detect non-constant expectations in the matrix of VaR-violations. Second, we propose χ2-tests for detecting cross-sectional and serial dependence in the VaR-forecasts. Moreover, we combine our new backtests with a test of unconditional coverage to yield two new backtests of multivariate conditional coverage. Results from a simulation study underline the usefulness of our new backtests for controlling portfolio risks across a bank’s business lines. In an empirical study, we show how our multivariate backtests can be employed by regulators to backtest a banking system.

Suggested Citation

  • Wied, Dominik & Weiß, Gregor N.F. & Ziggel, Daniel, 2016. "Evaluating Value-at-Risk forecasts: A new set of multivariate backtests," Journal of Banking & Finance, Elsevier, vol. 72(C), pages 121-132.
  • Handle: RePEc:eee:jbfina:v:72:y:2016:i:c:p:121-132
    DOI: 10.1016/j.jbankfin.2016.07.014
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    References listed on IDEAS

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    1. Bertrand Candelon & Gilbert Colletaz & Christophe Hurlin & Sessi Tokpavi, 2011. "Backtesting Value-at-Risk: A GMM Duration-Based Test," Journal of Financial Econometrics, Oxford University Press, vol. 9(2), pages 314-343, Spring.
    2. Zhou Zhou, 2013. "Heteroscedasticity and Autocorrelation Robust Structural Change Detection," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(502), pages 726-740, June.
    3. Berkowitz, Jeremy, 2001. "Testing Density Forecasts, with Applications to Risk Management," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(4), pages 465-474, October.
    4. Alexander Aue & Lajos Horváth, 2013. "Structural breaks in time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(1), pages 1-16, January.
    5. Elena-Ivona Dumitrescu & Christophe Hurlin & Vinson Pham, 2012. "Backtesting Value-at-Risk: From Dynamic Quantile to Dynamic Binary Tests," Finance, Presses universitaires de Grenoble, vol. 33(1), pages 79-112.
    6. Jeremy Berkowitz & Peter Christoffersen & Denis Pelletier, 2011. "Evaluating Value-at-Risk Models with Desk-Level Data," Management Science, INFORMS, vol. 57(12), pages 2213-2227, December.
    7. Gonçalves, Sílvia & White, Halbert, 2002. "The Bootstrap Of The Mean For Dependent Heterogeneous Arrays," Econometric Theory, Cambridge University Press, vol. 18(6), pages 1367-1384, December.
    8. Peter Christoffersen, 2004. "Backtesting Value-at-Risk: A Duration-Based Approach," Journal of Financial Econometrics, Oxford University Press, vol. 2(1), pages 84-108.
    9. 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 841-862, November.
    10. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    11. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    12. Christophe Hurlin & Sessi Tokpavi, 2006. "Backtesting Value at Risk Accuracy : A New Simple Test," Post-Print halshs-00257520, HAL.
    13. Escanciano, J. Carlos & Olmo, Jose, 2010. "Backtesting Parametric Value-at-Risk With Estimation Risk," Journal of Business & Economic Statistics, American Statistical Association, vol. 28(1), pages 36-51.
    14. Cristina Danciulescu, 2010. "Backtesting Value-at-Risk Models: A Multivariate Approach," Caepr Working Papers 2010-004, Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington.
    15. 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.).
    16. Ziggel, Daniel & Berens, Tobias & Weiß, Gregor N.F. & Wied, Dominik, 2014. "A new set of improved Value-at-Risk backtests," Journal of Banking & Finance, Elsevier, vol. 48(C), pages 29-41.
    17. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    18. Cristina Danciulescu, 2010. "Backtesting Value-at-Risk Models: A Multivariate Approach," CAEPR Working Papers 2010-004, Center for Applied Economics and Policy Research, Department of Economics, Indiana University Bloomington.
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    Cited by:

    1. Nikola RADIVOJEVIĆ & Luka FILIPOVI & Тomislav D. BRZAKOVIĆ, 2020. "A New Semiparametric Mirrored Historical Simulation Value-At-Risk Model," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(1), pages 5-21, March.
    2. Thiele, Stephen, 2019. "Detecting underestimates of risk in VaR models," Journal of Banking & Finance, Elsevier, vol. 101(C), pages 12-20.
    3. Denisa Banulescu-Radu & Christophe Hurlin & Jérémy Leymarie & Olivier Scaillet, 2021. "Backtesting Marginal Expected Shortfall and Related Systemic Risk Measures," Management Science, INFORMS, vol. 67(9), pages 5730-5754, September.
    4. Holger Fink & Andreas Fuest & Henry Port, 2018. "The Impact of Sovereign Yield Curve Differentials on Value-at-Risk Forecasts for Foreign Exchange Rates," Risks, MDPI, vol. 6(3), pages 1-19, August.
    5. Marta Małecka, 2021. "Testing for a serial correlation in VaR failures through the exponential autoregressive conditional duration model," Statistics in Transition New Series, Polish Statistical Association, vol. 22(1), pages 145-162, March.
    6. Kratz, Marie & Lok, Yen H. & McNeil, Alexander J., 2018. "Multinomial VaR backtests: A simple implicit approach to backtesting expected shortfall," Journal of Banking & Finance, Elsevier, vol. 88(C), pages 393-407.
    7. Gordy, Michael B. & McNeil, Alexander J., 2020. "Spectral backtests of forecast distributions with application to risk management," Journal of Banking & Finance, Elsevier, vol. 116(C).
    8. Jean-Paul Laurent & Hassan Omidi Firouzi, 2022. "Market Risk and Volatility Weighted Historical Simulation After Basel III," Working Papers hal-03679434, HAL.
    9. Leung, Melvern & Li, Youwei & Pantelous, Athanasios A. & Vigne, Samuel A., 2021. "Bayesian Value-at-Risk backtesting: The case of annuity pricing," European Journal of Operational Research, Elsevier, vol. 293(2), pages 786-801.
    10. Codrut Florin Ivascu & Daniela Serban, 2023. "Value at Risk Estimation for Non-Gaussian Distributions," The Review of Finance and Banking, Academia de Studii Economice din Bucuresti, Romania / Facultatea de Finante, Asigurari, Banci si Burse de Valori / Catedra de Finante, vol. 15(2), pages 181-190, December.
    11. Małecka Marta, 2021. "Testing for a serial correlation in VaR failures through the exponential autoregressive conditional duration model," Statistics in Transition New Series, Polish Statistical Association, vol. 22(1), pages 145-162, March.

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    More about this item

    Keywords

    Model risk; Multivariate backtesting; Value-at-Risk; Systemic risk;
    All these keywords.

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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