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Detecting structural breaks in realized volatility

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  • Song, Junmo
  • Baek, Changryong

Abstract

This paper considers the detection of structural changes in realized volatility based on HAR–GARCH models. For this, we propose a quasi-likelihood based score test for parameter changes in HAR–GARCH models. We derive the limiting null distribution of the score test by first introducing the quasi-maximum likelihood estimator to the HAR–GARCH model and establishing its asymptotic properties. The proposed test statistic is shown to converge weakly to a function of the Brownian bridge under the null of no structural change. Our simulations study shows reasonable sizes and powers of the test, even for non-Gaussian innovations. A real data application to S&P 500 realized volatility over the last 12 years coincides with three waves of financial crisis, namely the US housing, European sovereign debt, and emerging market crisis.

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  • Song, Junmo & Baek, Changryong, 2019. "Detecting structural breaks in realized volatility," Computational Statistics & Data Analysis, Elsevier, vol. 134(C), pages 58-75.
    • Handle: RePEc:eee:csdana:v:134:y:2019:i:c:p:58-75
    DOI: 10.1016/j.csda.2018.12.007
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    References listed on IDEAS

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    1. Taewook Lee & Moosup Kim & Changryong Baek, 2015. "Tests for Volatility Shifts in Garch Against Long-Range Dependence," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(2), pages 127-153, March.
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    Cited by:

    1. Byungsoo Kim & Junmo Song & Changryong Baek, 2021. "Robust test for structural instability in dynamic factor models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(4), pages 821-853, August.
    2. Lee, Taewook & Baek, Changryong, 2020. "Block wild bootstrap-based CUSUM tests robust to high persistence and misspecification," Computational Statistics & Data Analysis, Elsevier, vol. 150(C).

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