: A Semiparametric Approach to the Detection of Change-points in Volatility Dynamics of Financial Data (Huaiyu Hu - Boston University)

A Semiparametric Approach to the Detection of Change-points in Volatility Dynamics of Financial DataAbstract: One of the most important features of financial time series data is volatility. It is often the case that over time there are structural changes in the volatility, 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 based on 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 of 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 along with an efficient binary segmentation algorithm. The results show that in terms of the 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.

When 4:00 pm to 5:00 pm on Thursday, November 5, 2020
Location Online (Zoom) - Email Mickey Salins (msalins@bu.edu) for more information