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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 LLC, vol. 17(4), pages 901-915, December.
    • Handle: RePEc:tsj:stataj:v:17:y:2017:i:4:p:901-915
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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.
    2. Haining Chen & Prince Asare Vitenu-Sackey & Isaac Akpemah Bathuure, 2024. "Uncertainty Measures and Business Cycles: Evidence From the US," SAGE Open, , vol. 14(2), pages 21582440241, April.

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    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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