Detecting structural breaks in realized volatility

Junmo Song and Changryong Baek

Computational Statistics & Data Analysis, 2019, vol. 134, issue C, 58-75

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.

Keywords: Realized volatility; HAR–GARCH model; Long-memory process; Parameter change test; Score test (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:134:y:2019:i:c:p:58-75

DOI: 10.1016/j.csda.2018.12.007

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