Dynamic factor copula models with estimated cluster assignments

Dong Hwan Oh and Andrew J. Patton

Journal of Econometrics, 2023, vol. 237, issue 2

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 (search for similar items in EconPapers)
JEL-codes: C32 C38 C58 (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:237:y:2023:i:2:s0304407622002135

DOI: 10.1016/j.jeconom.2022.07.012

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