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The new KS method for a structural break detection in GARCH(1,1) models

Author

Listed:
  • Borzykh, Dmitriy

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

  • Yazykov, Artem

    (National Research University Higher School of Economics (NRU HSE); Federal Research Center «Computer Science and Control», Moscow Institute of Physics and Technology; Russian Federation)

Abstract

We propose a new method of a structural break detection for GARCH(1,1) model. This new method is called the KS method since it is based on Kolmogorov-Smirnov statistics. By using Monte-Carlo experiments we show that the KS method has good statistical properties. We compare our method with three well-known CUSUM methods: (Kokoszka, Leipus, 1999) referred to as KT method, (Inclán, Tiao, 1994) referred to as IT method, and (Lee et al., 2004) referred to as LTM method. To make the experiments closer to real conditions, we generate GARCH processes with coefficients estimated on 26 Russian stocks time series. Based on the results of numerical experiments, we suggest that our method is highly competitive and may be placed somewhere in between the KL method which has high power and high probability of type I error, and IT and LTM methods which have low power and also low probability of type I error.

Suggested Citation

  • Borzykh, Dmitriy & Yazykov, Artem, 2019. "The new KS method for a structural break detection in GARCH(1,1) models," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 54, pages 90-104.
    • Handle: RePEc:ris:apltrx:0369
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    References listed on IDEAS

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    1. Hernando Ombao & Jonathan Raz & Rainer von Sachs & Wensheng Guo, 2002. "The SLEX Model of a Non-Stationary Random Process," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 54(1), pages 171-200, March.
    2. Koichi Maekawa & Sangyeol & Lee, 2004. "The Cusum Test for Parameter Change in Regression with ARCH Errors," Econometric Society 2004 Far Eastern Meetings 606, Econometric Society.
    3. Kosei Fukuda, 2010. "Parameter changes in GARCH model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(7), pages 1123-1135.
    4. Richard A. Davis & Thomas C. M. Lee & Gabriel A. Rodriguez‐Yam, 2008. "Break Detection for a Class of Nonlinear Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 29(5), pages 834-867, September.
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    Cited by:

    1. Trifonov, Juri, 2023. "Modeling the risk premium in the Russian stock market considering the asymmetry effect," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 71, pages 5-19.

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    More about this item

    Keywords

    GARCH; volatility; 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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