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.

Corsi, F., Peluso, S., Audrino, F., Missing in Asynchronicity: A Kalman-em Approach for Multivariate Realized Covariance Estimation, <<JOURNAL OF APPLIED ECONOMETRICS>>, 2015; 30 (3): 377-397. [doi:10.1002/jae.2378] [http://hdl.handle.net/10807/66016]

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

Peluso, Stefano;
2015

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.
Inglese
Corsi, F., Peluso, S., Audrino, F., Missing in Asynchronicity: A Kalman-em Approach for Multivariate Realized Covariance Estimation, <<JOURNAL OF APPLIED ECONOMETRICS>>, 2015; 30 (3): 377-397. [doi:10.1002/jae.2378] [http://hdl.handle.net/10807/66016]
File in questo prodotto:
File Dimensione Formato  
KEM_JAE_3.pdf

accesso aperto

Tipologia file ?: Preprint (versione pre-referaggio)
Licenza: Creative commons
Dimensione 640.85 kB
Formato Adobe PDF
640.85 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/66016
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 29
  • ???jsp.display-item.citation.isi??? 29
social impact