| 主讲人简介： | Xiaohu Wang is an Associate Professor at the School of Economics in Fudan University. He obtained his PhD degree in economics from Singapore Management University and was an Assistant Professor at the Chinese University of Hong Kong before joining Fudan University. His research interest includes financial econometrics, time series analysis, empirical asset pricing, etc. He has published papers on Journal of Econometrics, Econometrics Journal, Journal of International Money and Finance, Econometric Reviews, Advances in Econometrics, and Economics Letters, and is now serving as an associate editor of China & World Economy. |
| 讲座简介： | In the presentation, I will discuss two papers related to rough volatility models. The first paper proposes to model and forecast realized volatility (RV) using the fractional Ornstein–Uhlenbeck (fO–U) process with a general Hurst parameter, H. A two-stage method is introduced for estimating parameters in the fO–U process based on discrete-sampled observations. All estimators have closed-form expressions and are easy to implement. A large sample theory of the proposed estimators is derived. We apply the model and the method to the logarithmic daily RV series of stock indexes in the US. The empirical findings suggest that H is much smaller than 1/2, indicating that the RV series have rough sample paths, and that the mean reversion parameter takes a small positive number, showing that the RV series are stationary but have slow mean reversion. In the second paper, we propose a revised rough volatility model with time-varying parameters to fit the RV series of crypto-currencies. A new estimation strategy is designed. Compared to many alternative models, including the ARFIMA, HAR, and HARQ models, the proposed model shows superior performance in out-of-sample forecasting. |
