Taking uncertainty seriously: simplicity versus complexity in financial regulation
Year: 2020
Type: article
Abstract: Distinguishing between risk and uncertainty, this article draws on the psychological literature on heuristics to consider whether and when simpler approaches may outperform more complex methods for modeling and regulating the financial system. We find that: simple methods can sometimes dominate more complex modeling approaches for calculating banks’ capital requirements, especially when data are limited or underlying risks are fat-tailed; simple indicators often outperformed more complex metrics in predicting individual bank failure during the global financial crisis; when combining different indicators to predict bank failure, simple and easy-to-communicate “fast-and-frugal” decision trees can perform comparably to standard, but more information-intensive, regressions. Taken together, our analyses suggest that because financial systems are better characterized by uncertainty than by risk, simpler approaches to modeling and regulating financial systems can usefully complement more complex ones and ultimately contribute to a safer financial system.
Source: Industrial and Corporate Change
DOI: 10.1093/icc/dtaa024
Authors: David Aikman, Mirta Galesic, Gerd Gigerenzer, Sujit Kapadia, Konstantinos Katsikopoulos, Amit Kothiyal, Emma Murphy, Tobias Neumann
Language: English
Subfield: Finance
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $4,299
OpenAlex ID: W2157157156