  Horizon scanning  One of these is that we are collecting evidence of the effects of regulations on and new risks faced by individual banks by pooling intelligence gathered in different parts of the Bank as part of a horizon scanning process. 27  25  Prudential Regulation Authority Policy Statement 22/17, https://www.bankofengland.co.uk/-/media/boe/files/prudentialregulation/policy-statement/2017/ps2217.pdf?la=en&hash=38A16F0135791C4803EC1948DBF86CFB8330DBCB. 26 Prudential Regulation Authority Policy Statement 12/17, https://www.bankofengland.co.uk/prudentialregulation/publication/2016/strengthening-accountability-in-banking-and-insurance-amendments-and-optimisations. 27 See ‘Looking both ways’, a speech by Sam Woods, 10 July 2017, https://www.bankofengland.co.uk/speech/2017/looking-both-ways. 11  All speeches are available online at www.bankofengland.co.uk/speeches  11  The process draws on supervisory and market intelligence, macroeconomic analysis, and thematic analysis of trends in key financial markets. With the information collected through this process we can then reflect on whether any developments warrant dynamic adjustments to be made to microprudential regulations.While horizon scanning helps us make decisions about dynamic adjustments, as well as supporting a well-informed supervisory oversight of banks’ behaviour, it does not absolve banks from their responsibilities to identify key risks as part of their own risk management, hold financial resources to mitigate those risks, or deal with the PRA in an open and cooperative way.   Evaluation work  The other way we are gathering evidence on the effects, including unintended consequences, regulations are having is running evaluation exercises. This is something that is happening internationally. A framework for post-implementation evaluations was developed by the Financial Stability Board in 2017 reforms.

And when data requirements are too large, the paper shows that simple but wrong (biased) models can outperform correct (unbiased) but complex models, precisely due to the large statistical variability (Charts 3, 4). The second reason is behavioural. Financial participants adapt their behaviours to the policy environment they face by finding ways to take on the same risk but using up less required capital – so-called “regulatory arbitrage”. 11, 12  A balance therefore needs to be struck between the need to allow for more risk-sensitive and complex measures to discourage risk shifting and the need to best trade off model bias and model variance, while being less vulnerable to arbitrage. 11  A well-known example of such behaviour is the use of securitisation and innovative forms of credit enhancement under the Basel I risk weights (see Jackson, P (1999), ‘Capital requirements and bank behaviour: the impact of the Basel Accord’, Basel Committee on Banking Supervision Working Paper No.1, https://www.bis.org/publ/bcbs_wp1.pdf). 12 Such behaviour is, of course, not limited to the financial system, or in fact to humans: even dolphins have been observed ‘gaming the rules’ (https://www.theguardian.com/science/2003/jul/03/research.science)! 5  All speeches are available online at www.bankofengland.co.uk/speeches  5  This is precisely what the final elements of the Basel III package seek to achieve. The package reaffirms the role of internal models as a primary means of guarding against risk shifting.