Publication Type
Journal Article
Version
publishedVersion
Publication Date
12-2023
Abstract
This paper proposes a dynamic multi-factor copula for use in high-dimensional time series applications. A novel feature of our model is that the assignment of individual variables to groups is estimated from the data, rather than being pre-assigned using SIC industry codes, market capitalization ranks, or other ad hoc methods. We adapt the k-means clustering algorithm for use in our application and show that it has excellent finite-sample properties. Applying the new model to returns on 110 US equities, we find around 20 clusters to be optimal. In out-of-sample forecasts, we find that a model with as few as five estimated clusters significantly outperforms an otherwise identical model with 21 clusters formed using two-digit SIC codes.
Keywords
High-dimensional models, Risk management, Multivariate density forecasting
Discipline
Econometrics | Finance
Research Areas
Econometrics
Publication
Journal of Econometrics
Volume
237
Issue
2
First Page
1
Last Page
23
ISSN
0304-4076
Identifier
10.1016/j.jeconom.2022.07.012
Publisher
Elsevier
Citation
OH, Dong Hwan and PATTON, Andrew John.
Dynamic factor copula models with estimated cluster assignments. (2023). Journal of Econometrics. 237, (2), 1-23.
Available at: https://ink.library.smu.edu.sg/soe_research/2885
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1016/j.jeconom.2022.07.012