Estimating Stochastic Volatility Diiusion Using Conditional Moments of Integrated Volatility

We exploit the distributional information contained in high-frequency intraday data in constructing a simple conditional moment estimator for stochastic volatility di usions. The estimator is based on the analytical solutions of the rst two conditional moments for the integrated volatility, which is e ectively approximated by the quadratic variation of the process. We successfully implement the resulting GMM estimator with high-frequency veminute foreign exchange and equity index returns. Our simulation evidence and actual empirical results indicate that the method is very reliable and accurate. The computational speed of the procedure compares very favorably to other existing estimation methods in the literature. JEL Classi cation: C13, C22.