Trunc-Opt vine building algorithms
D\'aniel Pfeifer, Edith Alice Kov\'acs

TL;DR
This paper introduces a novel approach for constructing truncated vine copulas that exploits conditional independences, improving upon existing methods by using a new scoring criterion and demonstrating better performance on real datasets.
Contribution
It proposes a new methodology for truncated vine construction based on the first tree after truncation, utilizing a novel score called the Weight of the truncated vine, and incorporates conditional independences into the algorithms.
Findings
The new algorithms outperform existing methods on real datasets.
Exploiting conditional independences improves the efficiency of vine construction.
The proposed score effectively guides the fitting of truncated vines.
Abstract
Vine copula models have become highly popular and practical tools for modelling multivariate probability distributions due to their flexibility in modelling different kinds of dependences between the random variables involved. However, their flexibility comes with the drawback of a high-dimensional parameter space. To tackle this problem, truncated vine copulas were introduced by Kurowicka (2010) (Gaussian case) and Brechmann and Czado (2013) (general case). Truncated vine copulas contain conditionally independent pair copulas after the truncation level. So far, in the general case, truncated vine constructing algorithms started from the lowest tree in order to encode the largest dependences in the lower trees. The novelty of this paper starts from the observation that a truncated vine is determined by the first tree after the truncation level (see Kov\'acs and Sz\'antai (2017)). This…
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Taxonomy
TopicsFinancial Risk and Volatility Modeling · Statistical Methods and Inference · Bayesian Methods and Mixture Models
