Non-stationary Financial Risk Factors and Macroeconomic Vulnerability for the UK

TL;DR

This paper introduces non-stationary factor models to better capture tail risks and financial stress in the UK, providing a more responsive indicator for macroeconomic vulnerability and financial stability policy.

Contribution

It proposes a novel non-stationary factor modeling approach for financial stress, capturing tail events and inertia, unlike traditional stationary models.

Findings

Non-stationary factors effectively capture tail risks.

The model improves growth-at-risk assessments.

Financial stress indicators show high inertia.

Abstract

Tracking the build-up of financial vulnerabilities is a key component of financial stability policy. Due to the complexity of the financial system, this task is daunting, and there have been several proposals on how to manage this goal. One way to do this is by the creation of indices that act as a signal for the policy maker. While factor modelling in finance and economics has a rich history, most of the applications tend to focus on stationary factors. Nevertheless, financial stress (and in particular tail events) can exhibit a high degree of inertia. This paper advocates moving away from the stationary paradigm and instead proposes non-stationary factor models as measures of financial stress. Key advantage of a non-stationary factor model is that while some popular measures of financial stress describe the variance-covariance structure of the financial stress indicators, the new index can capture the tails of the distribution. To showcase this, we use the obtained factors as variables in a growth-at-risk exercise. This paper offers an overview of how to construct non-stationary dynamic factors of financial stress using the UK financial market as an example.