A cluster driven log-volatility factor model: a deepening on the source of the volatility clustering

TL;DR

This paper introduces a novel log-volatility factor model that uses clustering to identify market and local contributions to volatility clustering, outperforming traditional models in statistical relevance and interpretability.

Contribution

The paper presents a cluster-driven factor model for log volatilities that automatically determines the number of factors using the DBHT algorithm and analyzes their role in volatility clustering.

Findings

Market globally contributes to volatility clustering.

Certain clusters have a significant local contribution.

The model explains similar volatility memory as PCA and exploratory factor models.

Abstract

We introduce a new factor model for log volatilities that performs dimensionality reduction and considers contributions globally through the market, and locally through cluster structure and their interactions. We do not assume a-priori the number of clusters in the data, instead using the Directed Bubble Hierarchical Tree (DBHT) algorithm to fix the number of factors. We use the factor model and a new integrated non parametric proxy to study how volatilities contribute to volatility clustering. Globally, only the market contributes to the volatility clustering. Locally for some clusters, the cluster itself contributes statistically to volatility clustering. This is significantly advantageous over other factor models, since the factors can be chosen statistically, whilst also keeping economically relevant factors. Finally, we show that the log volatility factor model explains a similar amount of memory to a Principal Components Analysis (PCA) factor model and an exploratory factor model.