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Dynamic factor copula models with estimated cluster assignments
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DOI：10.1016/j.jeconom.2022.07.012.png)
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Abstract
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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.(c) 2023 Elsevier B.V. All rights reserved.
Keywords:
High-dimensional models
Risk management
Multivariate density forecasting
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