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

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 microstruc...

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Main Authors:	Liu, Cheng, TANG, Cheng Yong
Format:	text
Language:	English
Published:	Institutional Knowledge at Singapore Management University 2013
Subjects:	EM algorithm High frequency data Integrated covariance matrix Kalman Filter Microstructure noise Missing data Quasi-maximum likelihood State Space Model Econometrics Economic Theory
Online Access:	https://ink.library.smu.edu.sg/lkcsb_research/5603 https://ink.library.smu.edu.sg/context/lkcsb_research/article/6602/viewcontent/A_state_space_model_approach_to_integrated_covariance_matrix_estimation_with_high_frequency_data.pdf
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Institution:	Singapore Management University
Language:	English

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spelling	sg-smu-ink.lkcsb_research-66022019-08-28T03:05:13Z A state space model approach to integrated covariance matrix estimation with high frequency data Liu, Cheng TANG, Cheng Yong 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 2013-12-01T08:00:00Z text application/pdf https://ink.library.smu.edu.sg/lkcsb_research/5603 https://ink.library.smu.edu.sg/context/lkcsb_research/article/6602/viewcontent/A_state_space_model_approach_to_integrated_covariance_matrix_estimation_with_high_frequency_data.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection Lee Kong Chian School Of Business eng Institutional Knowledge at Singapore Management University EM algorithm High frequency data Integrated covariance matrix Kalman Filter Microstructure noise Missing data Quasi-maximum likelihood State Space Model Econometrics Economic Theory
institution	Singapore Management University
building	SMU Libraries
continent	Asia
country	Singapore Singapore
content_provider	SMU Libraries
collection	InK@SMU
language	English
topic	EM algorithm High frequency data Integrated covariance matrix Kalman Filter Microstructure noise Missing data Quasi-maximum likelihood State Space Model Econometrics Economic Theory
spellingShingle	EM algorithm High frequency data Integrated covariance matrix Kalman Filter Microstructure noise Missing data Quasi-maximum likelihood State Space Model Econometrics Economic Theory Liu, Cheng TANG, Cheng Yong A state space model approach to integrated covariance matrix estimation with high frequency data
description	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
format	text
author	Liu, Cheng TANG, Cheng Yong
author_facet	Liu, Cheng TANG, Cheng Yong
author_sort	Liu, Cheng
title	A state space model approach to integrated covariance matrix estimation with high frequency data
title_short	A state space model approach to integrated covariance matrix estimation with high frequency data
title_full	A state space model approach to integrated covariance matrix estimation with high frequency data
title_fullStr	A state space model approach to integrated covariance matrix estimation with high frequency data
title_full_unstemmed	A state space model approach to integrated covariance matrix estimation with high frequency data
title_sort	state space model approach to integrated covariance matrix estimation with high frequency data
publisher	Institutional Knowledge at Singapore Management University
publishDate	2013
url	https://ink.library.smu.edu.sg/lkcsb_research/5603 https://ink.library.smu.edu.sg/context/lkcsb_research/article/6602/viewcontent/A_state_space_model_approach_to_integrated_covariance_matrix_estimation_with_high_frequency_data.pdf
_version_	1770574013289988096

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

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