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Heterogeneous Tail Generalized Common Factor Modeling


Hediger, Simon; Näf, Jeffrey; Paolella, Marc S; Polak, Pawel (2023). Heterogeneous Tail Generalized Common Factor Modeling. Digital Finance, 5:389-420.

Abstract

A multivariate normal mean-variance heterogeneous tails mixture distribution is proposed for the joint distribution of financial factors and asset returns (referred to as Factor-HGH). The proposed latent variable model incorporates a Cholesky decomposition of the dispersion matrix to ensure a rich dependency structure for capturing the stylized facts of the data. It generalizes several existing model structures, with or without financial factors. It is further applicable in large dimensions due to a fast ECME estimation algorithm of all the model parameters. The advantages of modelling financial factors and asset returns jointly under non-Gaussian errors are illustrated in an empirical comparison study between the proposed Factor-HGH model and classical financial factor models. While the results for the Fama-French 49 industry portfolios are in line with Gaussian-based models, in the case of highly tail heterogeneous cryptocurrencies, the portfolio based on the Factor HGH model doubles the average return while keeping the volatility, the maximum drawdown, the turnover, and the expected-shortfall at a low level.

Abstract

A multivariate normal mean-variance heterogeneous tails mixture distribution is proposed for the joint distribution of financial factors and asset returns (referred to as Factor-HGH). The proposed latent variable model incorporates a Cholesky decomposition of the dispersion matrix to ensure a rich dependency structure for capturing the stylized facts of the data. It generalizes several existing model structures, with or without financial factors. It is further applicable in large dimensions due to a fast ECME estimation algorithm of all the model parameters. The advantages of modelling financial factors and asset returns jointly under non-Gaussian errors are illustrated in an empirical comparison study between the proposed Factor-HGH model and classical financial factor models. While the results for the Fama-French 49 industry portfolios are in line with Gaussian-based models, in the case of highly tail heterogeneous cryptocurrencies, the portfolio based on the Factor HGH model doubles the average return while keeping the volatility, the maximum drawdown, the turnover, and the expected-shortfall at a low level.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Banking and Finance
Dewey Decimal Classification:330 Economics
Scope:Discipline-based scholarship (basic research)
Language:English
Date:28 April 2023
Deposited On:18 Aug 2023 10:14
Last Modified:13 Mar 2024 15:20
Publisher:Springer
ISSN:2524-6984
Additional Information:Bereits als Working Paper in SSRN erschienen: https://dx.doi.org/10.2139/ssrn.3951806.
OA Status:Hybrid
Free access at:Publisher DOI. An embargo period may apply.
Publisher DOI:https://doi.org/10.1007/s42521-023-00083-z
Related URLs:https://www.zora.uzh.ch/id/eprint/208572/
https://dx.doi.org/10.2139/ssrn.3951806
Other Identification Number:merlin-id:23569
  • Content: Published Version
  • Language: English
  • Licence: Creative Commons: Attribution 4.0 International (CC BY 4.0)