Dynamic Covariance Estimation under Structural Assumptions via a Joint Optimization Approach
Dynamic covariance estimation is a problem of fundamental importance in statistics, econometrics, with important applications in finance, especially portfolio optimization. While there is a large body of work on static covariance estimation, the current literature on dynamic covariance estimation is...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
ACM|4th ACM International Conference on AI in Finance
2023
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| Online Access: | https://hdl.handle.net/1721.1/153141 |
| Summary: | Dynamic covariance estimation is a problem of fundamental importance in statistics, econometrics, with important applications in finance, especially portfolio optimization. While there is a large body of work on static covariance estimation, the current literature on dynamic covariance estimation is somewhat limited in comparison. We propose a flexible optimization framework to simultaneously learn covariance matrices across different time periods under suitable structural assumptions on the period-specific covariance matrices and time-varying regularizers. We propose a novel efficient joint optimization algorithm to learn the covariance matrices simultaneously. Our numerical experiments demonstrate the computation improvements of our algorithm over both off-the-shelf solvers and other dynamic covariance estimation methods. We also see notable gains in terms of test MSE and downstream portfolio optimization tasks on both synthetic and real datasets. |
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