Missing in Asynchronicity: A Kalman-EM Approach for Multivariate Realized Covariance Estimation
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fulltext etc.
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no fulltext attached
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versione breve
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Motivated by the need for an unbiased and 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. In the expectation step,
by means of the Kalman filter with missing data, we reconstruct the
smoothed and synchronized series of the latent price processes. In
the maximization step, we search for covariance matrices that
maximize the expected likelihood obtained with the reconstructed
price series. Iterating between the two EM steps, we obtain a
KEM-improved covariance matrix estimate which is robust to both
asynchronicity and microstructure noise, and positive-semidefinite
by construction.
Extensive Monte Carlo simulations show the superior performance of
the KEM estimator over several alternative covariance matrix
estimates introduced in the literature. The application of the KEM
estimator in practice is illustrated on a 10-dimensional US stock
data set.
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tipo
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bozza lavoro (English)
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parole chiave
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High frequency data; Realized covariance matrix; Market microstructure noise; Missing data; Kalman filter; EM algorithm; Maximum likelihood
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data di apparenza
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2012
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Editore
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SEPS Working Paper Series (St. Gallen)
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review
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not review
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profile area
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SEPS - Quantitative Economic Methods
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citation
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Audrino, F., Corsi, F., & Peluso, S. (2012). Missing in
Asynchronicity: A Kalman-EM Approach for Multivariate Realized
Covariance Estimation. St. Gallen: SEPS Working Paper Series.
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