Tuesday, September 8 @ 11:00 - 12:30 PM (ONLINE)
Modeling the dynamics of the realized variance based on high-frequency data
Dangxing Chen, UC Berkeley
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.
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