Publication Type

Journal Article

Publication Date

1-2013

Abstract

We consider a state space model approach forhigh frequency financial data analysis. An expectationmaximization(EM) algorithm is developed for estimatingthe integrated covariance matrix of the assets. The statespace model with the EM algorithm can handle noisy financialdata with correlated microstructure noises. Difficultydue to asynchronous and irregularly spaced trading data ofmultiple assets can be naturally overcome by consideringthe problem in a scenario with missing data. Since the statespace model approach requires no data synchronization, norecord in the financial data is deleted so that it efficientlyincorporates information from all observations. Empiricaldata analysis supports the general specification of the statespace model, and simulations confirm the efficiency

Keywords

EM algorithm, High frequency data, Integrated covariance matrix, Kalman Filter, Microstructure noise, Missing data, Quasi-maximum likelihood, State Space Model

Discipline

Econometrics | Economic Theory

Publication

Statistics and Its Interface

Volume

6

Issue

4

First Page

463

Last Page

475

ISSN

1938-7989

Publisher

International Press

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Share

COinS