Evaluating the role of risk networks on risk identification, classification and emergence

TL;DR

This paper investigates the topology of risk networks derived from financial data, revealing modular structures and risk specialization patterns, and discusses their implications for risk classification and systemic risk analysis.

Contribution

It introduces an alternative methodology for generating weighted risk networks and applies it to empirical financial data, addressing biases in survey-based approaches.

Findings

Risk networks exhibit a modular topology.

Firms tend to specialize in certain risk classes.

Some risks have systemic impacts and emerging characteristics.

Abstract

Modern society heavily relies on strongly connected, socio-technical systems. As a result, distinct risks threatening the operation of individual systems can no longer be treated in isolation. Consequently, risk experts are actively seeking for ways to relax the risk independence assumption that undermines typical risk management models. Prominent work has advocated the use of risk networks as a way forward. Yet, the inevitable biases introduced during the generation of these survey-based risk networks limit our ability to examine their topology, and in turn challenge the utility of the very notion of a risk network. To alleviate these concerns, we proposed an alternative methodology for generating weighted risk networks. We subsequently applied this methodology to an empirical dataset of financial data. This paper reports our findings on the study of the topology of the resulting risk network. We observed a modular topology, and reasoned on its use as a robust risk classification framework. Using these modules, we highlight a tendency of specialization during the risk identification process, with some firms being solely focused on a subset of the available risk classes. Finally, we considered the independent and systemic impact of some risks and attributed possible mismatches to their emerging nature.