Abstract
Distinguishing between risk and uncertainty, this paper draws on the psychological literature on heuristics to consider whether and when simpler approaches may outperform more complex methods for modelling and regulating the financial system. We find that: (i) simple methods can sometimes dominate more complex modelling approaches for calculating banks’ capital requirements, especially if limited data are available for estimating models or the underlying risks are characterised by fat-tailed distributions; (ii) simple indicators often outperformed more complex metrics in predicting individual bank failure during the global financial crisis; and (iii) when combining information from different indicators to predict bank failure, ‘fast-and-frugal’ decision trees can perform comparably to standard, but more information-intensive, regression techniques, while being simpler and easier to communicate.
Highlights
The financial system has become increasingly complex over recent years
This is reflected in elements of the approach toward banking regulation that 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
Our analysis suggests that simpler rule-of-thumb approaches toward risk weighting, such as those provided by the Basel II standardized approach for rated portfolios or the even simpler Basel I approach, appear to have better outcomes, though it should be emphasized that they do not address the distinct issue of the serious overall under-capitalization that emerged under Basel I
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 modeling and risk management strategies or ever lengthening regulatory rulebooks. This neither helped to predict, nor to prevent, the global financial crisis. 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 toward banking regulation that 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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