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The refinement procedure of ICSS algorithm for structural breaks detection in GARCH-models

Author

Listed:
  • Borzykh, Dmitriy

    (National Research University Higher School of Economics (NRU HSE), Moscow, Russian Federation)

  • Khasykov, Mikhail

    (National Research University Higher School of Economics (NRU HSE), Moscow, Russian Federation)

Abstract

We suggest a hybrid algorithm for structural breaks detection when using a class of piecewise-specified GARCH(1,1) models. The algorithm comprises two steps. In the first step the moments of structural breaks are detected using KL-ICSS method based on (Kokoszka, Leipus, 1999) and (Inclán, Tiao, 1994). In the second step previously detected moments of structural breaks are refined with the help of a modified MML method. Therefore, the whole procedure is called ML-KL-ICSS algorithm. We also provide five numeric experiments to show the overall performance of the proposed procedure. Four of five experiments show that ML-KL-ICSS method is significantly more accurate in detecting structural breaks as opposed to one-step procedures. In one experiment the accuracy of both methods was comparable but ML-KL-ICSS method performed slightly better. Finally, we test our method using real data. In order to do that we detect structural breaks in common stocks returns volatility for the Russian “Gazprom” company. Detected moments of structural breaks correspond to significant events in the Russian economy.

Suggested Citation

  • Borzykh, Dmitriy & Khasykov, Mikhail, 2018. "The refinement procedure of ICSS algorithm for structural breaks detection in GARCH-models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 51, pages 126-139.
    • Handle: RePEc:ris:apltrx:0352
    as

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    References listed on IDEAS

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    More about this item

    Keywords

    GARCH; volatility; multiple change points; structural breaks; ICSS; CUSUM.;
    All these keywords.

    JEL classification:

    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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