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

Stefano Peluso, Fulvio Corsi, Francesco Audrino

Risultato della ricerca: Contributo in rivistaArticolo in rivista

19 Citazioni (Scopus)

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.
Lingua originaleEnglish
pagine (da-a)377-397
Numero di pagine21
RivistaJournal of Applied Econometrics
Volume30
DOI
Stato di pubblicazionePubblicato - 2015

Keywords

  • Multivariate Realized Covariance Estimation

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