Dynamic Covariance Estimation under Structural Assumptions via a Joint Optimization Approach
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3604237.3626885.pdf
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645.59 KB
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Author(s)
Chen, Wenyu
Benbaki, Riade
Zhu, Yada
Mazumder, Rahul
Date Issued
November 27, 2023
Publisher
ACM|4th ACM International Conference on AI in Finance
Citation
Chen, Wenyu, Benbaki, Riade, Zhu, Yada and Mazumder, Rahul. 2023. "Dynamic Covariance Estimation under Structural Assumptions via a Joint Optimization Approach."
Version
Final published version
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Operations Research Center
MIT-IBM Watson AI Lab
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1145/3604237.3626885
