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Dynamic Factor Correlations
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AI
DOI：10.1002/jae.70062.png)
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Abstract
En 中文
We introduce a dynamic factor correlation model whose core methodological innovation is a variation-free parametrization of dynamic factor loadings, inspired by the generalized Fisher transformation. The model accommodates time-varying correlations, heterogeneous heavy tails, and dependent idiosyncratic shocks. Applied to a Small Universe of 12 assets and a Large Universe of 323 stocks, the factor structure induces a sparse idiosyncratic correlation matrix with dependencies concentrated within subindustries, enabling scalability to high dimensions under a sparse block structure. Both factor loadings and correlations vary substantially. Allowing for heterogeneous heavy tails via convolution- distributions yields sizable improvements relative to Gaussian and multivariate- benchmarks.
Keywords:
block correlation matrix
factor structure
heavy-tailed distributions
high-dimensional modeling
multivariate GARCH
Journal
J
IF:
3.1
Papers:
55
Citations:
8.0K
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