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Automatic portmanteau tests with applications to market risk management

Author

Listed:
  • Guangwei Zhu

    (Southwestern University of Finance and Economics)

  • Zaichao Du

    (Southwestern University of Finance and Economics)

  • Juan Carlos Escanciano

    (Indiana University)

Abstract

In this article, we review some recent advances in testing for serial correlation, provide code for implementation, and illustrate this code’s application to market risk forecast evaluation. We focus on the classic and widely used portman- teau tests and their data-driven versions. These tests are simple to implement for two reasons: First, the researcher does not need to specify the order of the tested autocorrelations, because the test automatically chooses this number. Second, its asymptotic null distribution is chi-squared with one degree of freedom, so there is no need to use a bootstrap procedure to estimate the critical values. We illustrate the wide applicability of this methodology with applications to forecast evaluation for market risk measures such as value-at-risk and expected shortfall. Copyright 2017 by StataCorp LP.

Suggested Citation

  • Guangwei Zhu & Zaichao Du & Juan Carlos Escanciano, 2017. "Automatic portmanteau tests with applications to market risk management," Stata Journal, StataCorp LP, vol. 17(4), pages 901-915, December.
    • Handle: RePEc:tsj:stataj:v:17:y:2017:i:4:p:901-915
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    References listed on IDEAS

    as
    1. Delgado, Miguel A. & Velasco, Carlos, 2011. "An Asymptotically Pivotal Transform of the Residuals Sample Autocorrelations With Application to Model Checking," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 946-958.
    2. Escanciano, J. Carlos & Lobato, Ignacio N., 2009. "An automatic Portmanteau test for serial correlation," Journal of Econometrics, Elsevier, vol. 151(2), pages 140-149, August.
    3. 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.
    4. Francq, Christian & Roy, Roch & Zakoian, Jean-Michel, 2005. "Diagnostic Checking in ARMA Models With Uncorrelated Errors," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 532-544, June.
    5. Zaichao Du & Juan Carlos Escanciano, 2017. "Backtesting Expected Shortfall: Accounting for Tail Risk," Management Science, INFORMS, vol. 63(4), pages 940-958, April.
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    Cited by:

    1. Carlotta Penone & Elisa Giampietri & Samuele Trestini, 2022. "Futures–spot price transmission in EU corn markets," Agribusiness, John Wiley & Sons, Ltd., vol. 38(3), pages 679-709, July.

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    More about this item

    Keywords

    dbptest; rtau; autocorrelation; consistency; power; Akaike’s information criterion; Schwarz’s Bayesian information criterion; market risk;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation

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