Fractional Stochastic Volatility Model

Speaker：Liu Xiaobin（Researcher at the School of Economics, Zhejiang University）

Host：LIU Yanchu, Associate Professor, Lingnan College

Time and Date：14:30-17:30, Jun. 17, 2022

Venue: Room 701, Lingnan MBA Building

Meeting Link: Tencent meeting number: 377-707-766

Abstract:

This paper introduces a discrete-time fractional stochastic volatility model (FSV) based on fractional Gaussian noise. The new model includes the standard stochastic volatility model as a special case and has the same limit as the fractional integrated stochastic volatility (FISV) model. A simulated maximum likelihood method, which maximizes the time-domain log-likelihood function calculated by the importance sampling technique, and a frequency-domain quasi maximum likelihood method (or quasi Whittle) are employed to estimate the model parameters. Simulation studies suggest that, while both estimation methods can accurately estimate the model, the simulated maximum likelihood method outperforms the quasi Whittle method. As an illustration, we fit the FSV and FISV models with the proposed estimation techniques to the S&P 500 composite index over a sample period spanning 45 years. Our results reveal that the volatilities of the data series are persistent and rough.

 Profile of the speaker:

Dr. Liu Xiaobin is currently a researcher and doctoral supervisor of the School of Economics of Zhejiang University, a researcher at Zhejiang University Financial Research Institute. The research interest is financial measurement and measurement theory, empirical asset pricing, and macroeconomics. Some research results are published in Review of Economics and Statistics, Journal of Econometrics, Journal of Business & Economic Statistics, and "Financial Research" and other domestic and foreign academic journals.