Networks, Dynamic Factors, and the Volatility Analysis of High-Dimensional Financial Series

TL;DR

This paper introduces a network-based approach to analyze the interdependencies of stock volatilities in the S&P100, utilizing a decomposition method to handle high dimensionality and assess systemic risk, especially during financial crises.

Contribution

It develops a novel Long-Run Variance Decomposition Network framework combined with a sparse VAR model to analyze systemic risk in high-dimensional financial data.

Findings

Financial firms are key sources of contagion.

Systemic risk was heightened during the 2007-2008 crisis.

The network approach reveals influential nodes in volatility transmission.

Abstract

We consider weighted directed networks for analysing, over the period 2000-2013, the interdependencies between volatilities of a large panel of stocks belonging to the S\&P100 index. In particular, we focus on the so-called {\it Long-Run Variance Decomposition Network} (LVDN), where the nodes are stocks, and the weight associated with edge $(i,j)$ represents the proportion of $h$-step-ahead forecast error variance of variable $i$ accounted for by variable $j$'s innovations. To overcome the curse of dimensionality, we decompose the panel into a component driven by few global, market-wide, factors, and an idiosyncratic one modelled by means of a sparse vector autoregression (VAR) model. Inversion of the VAR together with suitable identification restrictions, produces the estimated network, by means of which we can assess how {\it systemic} each firm is.~Our analysis demonstrates the prominent role of financial firms as sources of contagion, especially during the~2007-2008 crisis.