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

Corsi, Fulvio; Peluso, Stefano; Audrino, Francesco

doi:10.2139/ssrn.2000996

Cited by 22 publications

(21 citation statements)

References 62 publications

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“…During our work on this paper we were sent a copy of Corsi, Peluso, and Audrino (2012) in January 2012 which was carried out independently and concurrently with our work. This paper is distinct in a number of ways, most notably we have a fully developed econometric theory for the method under general conditions and our computations are somewhat different.…”

Section: Three Noteworthy Papersmentioning

confidence: 99%

Econometric analysis of multivariate realised QML: Estimation of the covariation of equity prices under asynchronous trading

Shephard

Xiu

2017

Journal of Econometrics

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Estimating the covariance and correlation between assets using high frequency data is challenging due to market microstructure effects and Epps effects. In this paper we extend Xiu's univariate QML approach to the multivariate case, carrying out inference as if the observations arise from an asynchronously observed vector scaled Brownian model observed with error. Under stochastic volatility the resulting QML estimator is positive semi-definite, uses all available data, is consistent and asymptotically mixed normal. The quasi-likelihood is computed using a Kalman filter and optimised using a relatively simple EM algorithm which scales well with the number of assets. We derive the theoretical properties of the estimator and prove that it achieves the efficient rate of convergence. We show how to make it achieve the non-parametric efficiency bound for this problem. The estimator is also analysed using Monte Carlo methods and applied on equity data that are distinct in their levels of liquidity.

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Section: Three Noteworthy Papersmentioning

confidence: 99%

Econometric analysis of multivariate realised QML: Estimation of the covariation of equity prices under asynchronous trading

Shephard

Xiu

2017

Journal of Econometrics

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“…Motivated by the use of similar techniques for extracting the ‘true’ latent price process from high‐frequency data contaminated by market microstructure noise (e.g. Owens and Steigerwald, ; Corsi et al ., ), we rely on a standard Kalman filter‐based approach to do so.…”

Section: Daily House Price Indicesmentioning

confidence: 99%

Daily House Price Indices: Construction, Modeling, and Longer‐run Predictions

Bollerslev

Patton

Wang

2015

J of Applied Econometrics

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SUMMARYWe construct daily house price indices for 10 major US metropolitan areas. Our calculations are based on a comprehensive database of several million residential property transactions and a standard repeat-sales method that closely mimics the methodology of the popular monthly Case-Shiller house price indices. Our new daily house price indices exhibit dynamic features similar to those of other daily asset prices, with mild autocorrelation and strong conditional heteroskedasticity of the corresponding daily returns. A relatively simple multivariate time series model for the daily house price index returns, explicitly allowing for commonalities across cities and GARCH effects, produces forecasts of longer-run monthly house price changes that are superior to various alternative forecast procedures based on lower-frequency data.

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“…This is actually the case considered in Shepard and Xiu (2012) and Crosi, Peluso, and Audrino (2012). A closer look at (6) reveals that when A is not a diagonal matrix, information in the observed components Y…”

Section: A11j A12jmentioning

confidence: 79%

“…We note two independent studies, Shepard and Xiu (2012) and Crosi, Peluso, and Audrino (2012), on high frequency financial data analysis using the EM algorithm. In both Shepard and Xiu (2012) and Crosi Peluso, and Audrino (2012), the microstructure noises are considered as uncorrelated between different assets.…”

Section: Introductionmentioning

confidence: 99%

A state space model approach to integrated covariance matrix estimation with high frequency data

Liu

Tang

2013

Statistics and Its Interface

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We consider a state space model approach for high frequency financial data analysis. An expectationmaximization (EM) algorithm is developed for estimating the integrated covariance matrix of the assets. The state space model with the EM algorithm can handle noisy financial data with correlated microstructure noises. Difficulty due to asynchronous and irregularly spaced trading data of multiple assets can be naturally overcome by considering the problem in a scenario with missing data. Since the state space model approach requires no data synchronization, no record in the financial data is deleted so that it efficiently incorporates information from all observations. Empirical data analysis supports the general specification of the state space model, and simulations confirm the efficiency gain and the benefit of the state space model approach.

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

Cited by 22 publications

References 62 publications

Econometric analysis of multivariate realised QML: Estimation of the covariation of equity prices under asynchronous trading

Econometric analysis of multivariate realised QML: Estimation of the covariation of equity prices under asynchronous trading

Daily House Price Indices: Construction, Modeling, and Longer‐run Predictions

A state space model approach to integrated covariance matrix estimation with high frequency data

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