An Information Filtering approach to stress testing: an application to FTSE markets

TL;DR

This paper introduces a new method for stress testing financial markets by modeling the impact and response to shocks using a sparse probabilistic elliptical model, revealing network-based shock patterns in FTSE markets.

Contribution

It develops a novel sparse inverse covariance modeling approach with information filtering networks to analyze market shock impacts and responses.

Findings

Central sectors are more affected by shocks.

Stocks with high diversification impact the market more.

Shock patterns change during crises, showing convergence.

Abstract

We present a novel methodology to quantify the "impact" of and "response" to market shocks. We apply shocks to a group of stocks in a part of the market, and we quantify the effects in terms of average losses on another part of the market using a sparse probabilistic elliptical model for the multivariate return distribution of the whole market. Sparsity is introduced with an $L_0$-norm regularization, which forces to zero some elements of the inverse covariance according to a dependency structure inferred from an information filtering network. Our study concerns the FTSE 100 and 250 markets and analyzes impact and response to shocks both applied to and received from individual stocks and group of stocks. We observe that the shock pattern is related to the structure of the network associated with the sparse structure of the inverse covariance of stock returns. Central sectors appear more likely to be affected by shocks, and stocks with a large level of underlying diversification have a larger impact on the rest of the market when experiencing shocks. By analyzing the system during times of crisis and comparative market calmness, we observe changes in the shock patterns with a convergent behavior in times of crisis.