Modelling of dependence in high-dimensional financial time series by cluster-derived canonical vines

TL;DR

This paper introduces a cluster-derived canonical vine (CDCV) copula model for high-dimensional financial time series, which derives hierarchical indexes from data clusters, reducing reliance on external indexes and improving dependence modeling.

Contribution

The paper presents a novel CDCV copula model that constructs hierarchical indexes from data clusters, enhancing high-dimensional dependence modeling without external index dependence.

Findings

Achieves comparable or better performance than models using external indexes.

Reduces parameter complexity in high-dimensional dependence modeling.

Demonstrates effective data-driven hierarchy construction.

Abstract

We extend existing models in the financial literature by introducing a cluster-derived canonical vine (CDCV) copula model for capturing high dimensional dependence between financial time series. This model utilises a simplified market-sector vine copula framework similar to those introduced by Heinen and Valdesogo (2008) and Brechmann and Czado (2013), which can be applied by conditioning asset time series on a market-sector hierarchy of indexes. While this has been shown by the aforementioned authors to control the excessive parameterisation of vine copulas in high dimensions, their models have relied on the provision of externally sourced market and sector indexes, limiting their wider applicability due to the imposition of restrictions on the number and composition of such sectors. By implementing the CDCV model, we demonstrate that such reliance on external indexes is redundant as we can achieve equivalent or improved performance by deriving a hierarchy of indexes directly from a clustering of the asset time series, thus abstracting the modelling process from the underlying data.