Working Paper Revision

Dynamic Factor Copula Models with Estimated Cluster Assignments

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;

JEL Classification: C32; C38; C58;

https://doi.org/10.17016/FEDS.2021.029r1

Bibliographic Information

Provider: Board of Governors of the Federal Reserve System (U.S.)

Part of Series: Finance and Economics Discussion Series

Publication Date: 2022-05-06

Number: 2021-029r1

Related Works

• Working Paper Revision: Dynamic Factor Copula Models with Estimated Cluster Assignments