Missing in Asynchronicity: A Kalman-EM Approach for Multivariate Realized Covariance Estimation
Journal
Journal of Applied Econometrics
ISSN
0883-7252
ISSN-Digital
1099-1255
Type
journal article
Date Issued
2015-05-01
Author(s)
Abstract
Motivated by the need of 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 an high-dimensional application on US stocks. KEM provides very accurate covariance matrix estimates and significantly outperforms alternative approaches recently introduced in the literature.
Language
English
Keywords
High frequency data
Realized covariance matrix
Missing data
Kalman filter
EM algorithm.
HSG Classification
contribution to scientific community
HSG Profile Area
SEPS - Quantitative Economic Methods
Refereed
Yes
Publisher
Wiley-Blackwell
Publisher place
Chichester
Volume
30
Number
3
Start page
377
End page
397
Pages
21
Subject(s)
Eprints ID
227469