Dangxing Chen, UC Berkeley
ABSTRACT: Abstract: This paper studies in some detail a class of continuous-time stochastic volatility models. These models are direct models of daily asset return volatility based on realized measures constructed from high-frequency data. The models are capable of capturing the mean-reversion effect and different rates of innovations. We propose a new robust hybrid method to estimate parameters of models, using the method of conditional moments for the drift term and the minimum-distance estimation for the diffusion term. Along with calibration techniques, rigorous goodness-of-fit statistical tests are conducted. Empirical results suggest that our method is very promising.