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: | , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2013
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| Subjects: | |
| 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 |
| Summary: | 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 |
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