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: | Chen, Wenyu, Benbaki, Riade, Zhu, Yada, Mazumder, Rahul |
---|---|
Other Authors: | Massachusetts Institute of Technology. Operations Research Center |
Format: | Article |
Language: | English |
Published: |
ACM|4th ACM International Conference on AI in Finance
2023
|
Online Access: | https://hdl.handle.net/1721.1/153141 |
Similar Items
-
Topics in Sparsity and Compression: From High dimensional
statistics to Overparametrized Neural Networks
by: Benbaki, Riade
Published: (2023) -
The Discrete Dantzig Selector: Estimating Sparse Linear Models via Mixed Integer Linear Optimization
by: Mazumder, Rahul, et al.
Published: (2019) -
Knowledge Graph Guided Simultaneous Forecasting and Network Learning for Multivariate Financial Time Series
by: Ibrahim, Shibal, et al.
Published: (2022) -
A new computational framework for log-concave density estimation
by: Chen, Wenyu, et al.
Published: (2024) -
Optimal Clock Synchronization Under Different Delay Assumptions
by: Attiya, Hagit, et al.
Published: (2023)