  One of the most important features of financial time series data is
volatility. There are often structural changes in volatility over time, and an
accurate estimation of the volatility of financial time series requires careful
identification of change-points. A common approach to modeling the volatility
of time series data is the well-known GARCH model. Although the problem of
change-point estimation of volatility dynamics derived from the GARCH model has
been considered in the literature, these approaches rely on parametric
assumptions of the conditional error distribution, which are often violated in
financial time series. This may lead to inaccuracies in change-point detection
resulting in unreliable GARCH volatility estimates. This paper introduces a
novel change-point detection algorithm based on a semiparametric GARCH model.
The proposed method retains the structural advantages of the GARCH process
while incorporating the flexibility of nonparametric conditional error
distribution. The approach utilizes a penalized likelihood derived from a
semiparametric GARCH model and an efficient binary segmentation algorithm. The
results show that in terms of change-point estimation and detection accuracy,
the semiparametric method outperforms the commonly used Quasi-MLE (QMLE) and
other variations of GARCH models in wide-ranging scenarios.
