City Research Online

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:	Symplectic Administrator

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