Informative Risk Measures in the Banking Industry: A Proposal based on the Magnitude-Propensity Approach

TL;DR

This paper introduces a new multivariate risk measure for the banking industry, enhancing traditional scalar measures by capturing the complex structure of potential losses and improving risk assessment for regulatory and managerial purposes.

Contribution

It proposes a novel multivariate risk representation based on the magnitude-propensity approach, extending classical methods with better interpretability and applicability to extreme events.

Findings

Demonstrates the framework's robustness with real-world data

Shows improved risk characterization over scalar measures

Highlights potential for regulatory and managerial use

Abstract

Despite decades of research in risk management, most of the literature has focused on scalar risk measures (like e.g. Value-at-Risk and Expected Shortfall). While such scalar measures provide compact and tractable summaries, they provide a poor informative value as they miss the intrinsic multivariate nature of risk.To contribute to a paradigmatic enhancement, and building on recent theoretical work by Faugeras and Pag\'es (2024), we propose a novel multivariate representation of risk that better reflects the structure of potential portfolio losses, while maintaining desirable properties of interpretability and analytical coherence. The proposed framework extends the classical frequency-severity approach and provides a more comprehensive characterization of extreme events. Several empirical applications based on real-world data demonstrate the feasibility, robustness and practical relevance of the methodology, suggesting its potential for both regulatory and managerial applications.