An AI approach to measuring financial risk

TL;DR

This paper introduces the Financial Risk Meter (FRM), an AI-based systemic risk measure derived from linear quantile lasso regression, validated through comparison with existing risk indicators and implemented with parallel computing tools.

Contribution

The paper presents a novel AI-driven systemic risk measure, the FRM, based on penalization parameters, and demonstrates its validity and implementation tools.

Findings

FRM correlates with VIX, SRISK, and Google Trends.

Mutual Granger causality exists between FRM and other risk measures.

FRM effectively captures systemic risk dynamics.

Abstract

AI artificial intelligence brings about new quantitative techniques to assess the state of an economy. Here we describe a new measure for systemic risk: the Financial Risk Meter (FRM). This measure is based on the penalization parameter (lambda) of a linear quantile lasso regression. The FRM is calculated by taking the average of the penalization parameters over the 100 largest US publicly traded financial institutions. We demonstrate the suitability of this AI based risk measure by comparing the proposed FRM to other measures for systemic risk, such as VIX, SRISK and Google Trends. We find that mutual Granger causality exists between the FRM and these measures, which indicates the validity of the FRM as a systemic risk measure. The implementation of this project is carried out using parallel computing, the codes are published on www.quantlet.de with keyword FRM. The R package RiskAnalytics is another tool with the purpose of integrating and facilitating the research, calculation and analysis methods around the FRM project. The visualization and the up-to-date FRM can be found on hu.berlin/frm.