City Research Online

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

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. 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: The version posted may not be updated or replaced with the VoR and must contain the text This is the accepted version of the following article: Corsi, F., Peluso, S. and Audrino, F. (2014), MISSING IN ASYNCHRONICITY: A KALMAN-EM APPROACH FOR MULTIVARIATE REALIZED COVARIANCE ESTIMATION. J. Appl. Econ., which has been published in final form at http://dx.doi.org/doi: 10.1002/jae.2378
Publisher Keywords: High frequency data, Realized covariance matrix, Missing data, Kalman filter, EM algorithm
Subjects: H Social Sciences > HB Economic Theory
Departments: School of Policy & Global Affairs > Economics
SWORD Depositor:
[thumbnail of CorsiPelusoAudrino_KEM_sub2013.pdf]
Preview
PDF - Accepted Version
Download (656kB) | Preview

Export

Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Downloads

Downloads per month over past year

View more statistics

Actions (login required)

Admin Login Admin Login