Estimation of Integrated Covolatility for Non-synchronous Assets in the Presence of Microstructure Noise

Abstract

The use of high-frequency return data has led to dramatic improvements in both theoretical and applied finance research. Estimators of covariance among multiple processes have been proposed, such as realized variance and Hayashi-Yoshida estimator. We are introducing our new estimator, the random lead-lag (RLL) estimator, which coincides with the Hayashi-Yoshida estimator at very high frequency. We studied the performance of RLL estimator both with and without microstructure noise for non-synchronous data and obtained the optimal estimator with good bias-variance trade-off. Our result is confirmed by simulation. We also applied our method to stock data from Nasdaq Exchange (1990). This is a joint work with Professor Rituparna Sen. KEY WORDS: High frequency data, microstructure noise, non-synchronicity, realized covariance estimator, Hayashi-Yoshida estimator, random lead-lag covariance estimator, bias-variance trade-off.