Abstract
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.
Highlights
The financial system has become increasingly complex over recent years
This is reflected in elements of the approach towards banking regulation which allows banks to use their own internal models to calculate regulatory capital requirements based upon underlying estimates of variables such as default probabilities and losses in the event of default, and has led to an exponential rise in the number of calculations required for a large, universal bank from single figures a generation ago to hundreds of thousands, perhaps even millions, today
This both emphasises the risks from regulatory arbitrage and other adverse incentive effects that would arise from focussing on just a single indicator such as the leverage ratio and highlights the possibility that indicators which appeared to signal well in the past may lose some of their predictive power when they become the subject of greater regulatory scrutiny
Summary
The financial system has become increasingly complex over recent years Both the private sector and public authorities have tended to meet this complexity head on, whether through increasingly complex modelling and risk management strategies or ever-lengthening regulatory rulebooks. The result has been a quest for ever-greater precision — and ever increasing complexity — in the models and toolkits typically being developed and used in applied work This is reflected in elements of the approach towards banking regulation which allows banks to use their own internal models to calculate regulatory capital requirements based upon underlying estimates of variables such as default probabilities and losses in the event of default, and has led to an exponential rise in the number of calculations required for a large, universal bank from single figures a generation ago to hundreds of thousands, perhaps even millions, today
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