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One of the most important features of financial time series data isvolatility. There are often structural changes in volatility over time, and anaccurate estimation of the volatility of financial time series requires carefulidentification of change-points. A common approach to modeling the volatilityof time series data is the well-known GARCH model. Although the problem ofchange-point estimation of volatility dynamics derived from the GARCH model hasbeen considered in the literature, these approaches rely on parametricassumptions of the conditional error distribution, which are often violated infinancial time series. This may lead to inaccuracies in change-point detectionresulting in unreliable GARCH volatility estimates. This paper introduces anovel change-point detection algorithm based on a semiparametric GARCH model.The proposed method retains the structural advantages of the GARCH processwhile incorporating the flexibility of nonparametric conditional errordistribution. The approach utilizes a penalized likelihood derived from asemiparametric GARCH model and an efficient binary segmentation algorithm. Theresults show that in terms of change-point estimation and detection accuracy,the semiparametric method outperforms the commonly used Quasi-MLE (QMLE) andother variations of GARCH models in wide-ranging scenarios.

A Semiparametric Approach to the Detection of Change-points in Volatility Dynamics of Financial Data