Heterogeneous tail generalized common factor modeling

• Published in: Digital finance 2023, Vol.5 (2), p.389-420
• Main Authors: Hediger, Simon, Näf, Jeffrey, Paolella, Marc S., Polak, Paweł
• Format: Article
• Language: English
• Subjects: Banking; Business Finance; Economics and Finance; Finance; Macroeconomics/Monetary Economics//Financial Economics; Original Article
• ISSN: 2524-6984; 2524-6186
• DOI: 10.1007/s42521-023-00083-z

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. 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 almost doubles the average return while keeping the volatility, the maximum drawdown, the turnover, and the expected shortfall at a low level.