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Robust Backtesting Tests for Value-at-risk Models

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  • J. Carlos Escanciano
  • Jose Olmo

Abstract

Backtesting methods are statistical tests designed to uncover value-at-risk (VaR) models not capable of reporting the correct unconditional coverage probability or filtering the serial dependence in the data. We show in this paper that these methods are subject to the presence of model risk produced by the incorrect specification of the conditional VaR model and derive its effect in the asymptotic distribution of the relevant out-of-sample tests. We also show that in the absence of estimation risk, the unconditional backtest is affected by model misspecification but the independence test is not. We propose using resampling methods to implement robust backtests. Our experiments suggest that block-bootstrap outperforms subsampling methods in size accuracy. We carry out a Monte Carlo study to see the importance of model risk in finite samples for location-scale models that are incorrectly specified but correct on "average ". An application to Dow--Jones Index shows the impact of correcting for model risk on backtesting procedures for different dynamic VaR models measuring risk exposure. C52, C53, G32 Copyright The Author 2010. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org, Oxford University Press.

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Bibliographic Info

Article provided by Society for Financial Econometrics in its journal Journal of Financial Econometrics.

Volume (Year): 9 (2011)
Issue (Month): 1 (Winter)
Pages: 132-161

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Handle: RePEc:oup:jfinec:v:9:y:2011:i:1:p:132-161

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Cited by:
  1. Köksal, Bülent & Orhan, Mehmet, 2012. "Market risk of developed and developing countries during the global financial crisis," MPRA Paper 37523, University Library of Munich, Germany.
  2. Evers, Corinna & Rohde, Johannes, 2014. "Model Risk in Backtesting Risk Measures," Hannover Economic Papers (HEP), Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät dp-529, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
  3. Gourieroux, Christian & Zakoïan, Jean-Michel, 2013. "Estimation-Adjusted Var," Econometric Theory, Cambridge University Press, Cambridge University Press, vol. 29(04), pages 735-770, August.
  4. Igor Kheifets, 2014. "Specification Tests for Nonlinear Dynamic Models," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University 1937, Cowles Foundation for Research in Economics, Yale University.
  5. Juan Carlos Escanciano & Pei Pei, 2012. "Pitfalls in Backtesting Historical Simulation VaR Models," Caepr Working Papers, Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington 2012-003, Center for Applied Economics and Policy Research, Economics Department, Indiana University Bloomington.

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