Risk, return, and investor behavior in the Chinese equity market - Fei, Tianlun (2020)
This thesis comprises two chapters with a focus on volatility estimating, modeling and forecasting using intraday data in the Chinese stock market. The first chapter explores the performance of two types of estimators in volatility prediction: the realized volatility (RV) type and duration-based ones. This is motivated by the theoretical and empirical support for both categories of estimators that are distinct from each other. I use intraday data for 203 component stocks in the CSI 300 index and adopt a combination of volatility models and these two types of estimators to produce 1-, 5- and 22-day ahead forecasts. I show that, although empirically more efficient with US data, the duration-based volatility estimators fail to compete statistically with the traditional RV-type although in a portfolio setting both types of estimators generate similar economic value to a mean-variance investor. A comprehensive simulation exercise is undertaken to rationalize the poorer statistical performance of duration-based estimators.