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Missing in Asynchronicity: A Kalman-em Approach for Multivariate Realized Covariance Estimation

Corsi, F., Peluso, S. and Audrino, F. (2015). Missing in Asynchronicity: A Kalman-em Approach for Multivariate Realized Covariance Estimation. Journal of Applied Econometrics, 30(3), pp. 377-397. doi: 10.1002/jae.2378

Abstract

Motivated by the need for a positive-semidefinite estimator of multivariate realized covariance matrices, we model noisy and asynchronous ultra-high-frequency asset prices in a state-space framework with missing data. We then estimate the covariance matrix of the latent states through a Kalman smoother and expectation maximization (KEM) algorithm. Iterating between the two EM steps, we obtain a covariance matrix estimate which is robust to both asynchronicity and microstructure noise, and positive-semidefinite by construction. We show the performance of the KEM estimator using extensive Monte Carlo simulations that mimic the liquidity and market microstructure characteristics of the S & P 500 universe as well as in a high-dimensional application on US stocks. KEM provides very accurate covariance matrix estimates and significantly outperforms alternative approaches recently introduced in the literature.

Publication Type: Article
Additional Information: This is the peer reviewed version of the following article: Corsi, F., Peluso, S. & Audrino, F. (2015). Missing in Asynchronicity: A Kalman-em Approach for Multivariate Realized Covariance Estimation. Journal of Applied Econometrics, 30(3), pp. 377-397, which has been published in final form at https://doi.org/10.1002/jae.2378. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
Publisher Keywords: High frequency data; Realized covariance matrix; Missing data; Kalman filter; EM algorithm
Departments: School of Arts & Social Sciences > Economics
URI: http://openaccess.city.ac.uk/id/eprint/19454
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