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Closed-Form Multi-Factor Copula Models With Observation-Driven Dynamic Factor Loadings

We develop new multi-factor dynamic copula models with time-varying factor loadings and observation-driven dynamics. The new models are highly flexible, scalable to high dimensions, and ensure positivity of covariance and correlation matrices. A closed-form likelihood expression allows for straightforward parameter estimation and likelihood inference. We apply the new model to a large panel of 100 U.S. stocks over the period 2001–2014. The proposed multi-factor structure is much better than existing (single-factor) models at describing stock return dependence dynamics in high-dimensions. The new factor models also improve one-step-ahead copula density forecasts and global minimum variance portfolio performance. Finally, we investigate different mechanisms to allocate firms into groups and find that a simple industry classification outperforms alternatives based on observable risk factors, such as size, value, or momentum.

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Informasi Detail

Judul Seri

JOURNAL OF BUSINESS AND ECONOMIC STATISTICS; Vol.39 No.4 October 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 1066 - 1079

Bahasa

English

ISBN/ISSN

-

Klasifikasi

NONE

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

FACTOR STRUCTURE
FACTOR COPULAS
MULTIVARIATE DENSITY FORECAST
SCORE-DRIVEN DYNAMICS

Info Detail Spesifik

https://doi.org/10.1080/07350015.2020.1763806

Pernyataan Tanggungjawab

Anne Opschoor, André Lucas, István Barra, Dick van Dijk

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