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A comparison of bootstrap and Monte-Carlo testing approaches to value-at-risk diagnosis

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Abstract

In this note we investigate a particular resampling scheme and Monte Carlo testing to determine critical values for two test statistics typically used for diagnosing value-at-risk models. In cases of small nominal coverage subjected to testing, the dynamic quantile test and a corresponding logit based likelihood ratio test suffer from poor convergence to the asymptotic limit distribution. In terms of empirical size both resampling and Monte Carlo approaches offer most accurate test features with the Monte Carlo technique achieving power gains if a misspecified value-at-risk model is subjected to testing.

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References

  • Christoffersen PF (1998) Evaluating interval forecasts. Int Econ Rev 39: 841–862

    Article  MathSciNet  Google Scholar 

  • Clements MP, Taylor N (2003) Evaluating interval forecasts of high-frequency financial data. J Appl Econom 18: 445–456

    Article  Google Scholar 

  • Dufour JM (2006) Monte Carlo tests with nuisance parameters: A general approach to finite-sample inference and nonstandard asymptotics. J Econom 133: 443–477

    Article  MathSciNet  Google Scholar 

  • Dufour JM, Khalaf L (2001) Monte Carlo test methods in econometrics. In: Baltagi B (eds) Companion to theoretical econometrics, chap 23. Blackwell, Oxford, pp 494–519

    Google Scholar 

  • Engle RF, Manganelli S (2004a) A comparison of value at risk models in finance. In: Szego G (eds) Risk measures for the 21st century. Wiley, Chichester, pp 123–144

    Google Scholar 

  • Engle RF, Manganelli S (2004b) CAViaR: Conditional autoregressive value at risk by regression quantiles. J Bus Econ Stat 22: 367–381

    Article  MathSciNet  Google Scholar 

  • Herwartz H, Xu F (2009) A new approach to bootstrap inference in functional coefficient models. Comput Stat Data Anal 53: 2155–2167

    Article  MATH  Google Scholar 

  • Patton AJ (2006) Modelling asymmetric exchange rate dependence. Int Econ Rev 47: 527–556

    Article  MathSciNet  Google Scholar 

  • Wu CFJ (1986) Jackknife, bootstrap, and other resampling methods in regression analysis (with discussion). Ann Stat 14: 1261–1343

    Article  MATH  Google Scholar 

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Authors and Affiliations

  1. Institute for Statistics and Econometrics, Christian-Albrechts-University of Kiel, Olshausenstr. 40, 24118, Kiel, Germany

    Helmut Herwartz

  2. Department of Economics, Christian-Albrechts-University of Kiel, Olshausenstr. 40, 24118, Kiel, Germany

    Israel Waichman

Authors
  1. Helmut Herwartz
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  2. Israel Waichman
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Correspondence to Israel Waichman.

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Herwartz, H., Waichman, I. A comparison of bootstrap and Monte-Carlo testing approaches to value-at-risk diagnosis. Comput Stat 25, 725–732 (2010). https://doi.org/10.1007/s00180-010-0194-4

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  • DOI: https://doi.org/10.1007/s00180-010-0194-4

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