Stocks in the Short Run

A multivariate positive definite estimator of the covariance matrix of noisy and asynchronously observed asset returns is proposed. We adopt a Bayesian Dynamic Linear Model which allows us to interprete microstructure noise as measurement errors, and the asynchronous trading as missing observations in an otherwise synchronous series. These missing observations are treated as any other parameter of the problem as typically done in a Bayesian framework. We use an augmented Gibbs algorithm and thus sample the covariance matrix, the observational error variance matrix, the latent process and the missing observations of the noisy process from their full conditional distributions. Convergence issues and robustness of the Gibbs sampler are discussed. A simulation study compares our Bayesian estimator with recently proposed pair-wise QMLE-type and Multivariate Realized Kernel estimators, under different liquidity and microstructure noise conditions. The results suggest that our estimator is superior in terms of RMSE in both a two-and ten-dimensional settings, especially with dispersed and high missing percentages and with high noise. This suggests that our Bayesian estimator is more robust in severe conditions, such as portfolios of assets with heterogeneous liquidity profiles, or particularly illiquid, or when there is a high level of microstructure noise in the market.

show abstract

A Bayesian High-Frequency Estimator of the Multivariate Covariance of Noisy and Asynchronous Returns

Peluso

Corsi

Mira

2014

JOURNAL OF FINANCIAL ECONOMETRICS

View full text Add to dashboard Cite

show abstract

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

2012

View full text Add to dashboard Cite

This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractMotivated 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.JEL classification: C13; C51; C52; C58

show abstract

A Bayesian High-Frequency Estimator of the Multivariate Covariance of Noisy and Asynchronous Returns

2012

View full text Add to dashboard Cite

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

Stocks in the Short Run

Cited by 3 publications

References 30 publications

A Bayesian High-Frequency Estimator of the Multivariate Covariance of Noisy and Asynchronous Returns

A Bayesian High-Frequency Estimator of the Multivariate Covariance of Noisy and Asynchronous Returns

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

A Bayesian High-Frequency Estimator of the Multivariate Covariance of Noisy and Asynchronous Returns

Contact Info

Product

Resources

About