In particular, the LCR and NSFR may complement each other to the extent there are differences in asset liquidity and funding stability at different maturities and that these differences evolve over the cycle. 21  All speeches are available online at www.bankofengland.co.uk/speeches  21  An Empirical Assessment of Regulatory Metrics  There are conceptual reasons why a portfolio approach to regulatory design, with a small, complementary set of constraints, may have merit in a robust control sense: addressing the different risks facing financial institutions and providing insurance against various uncertainties. Ultimately, however, the extent of over-identification, and any costs it might impose, will depend on the empirical distribution of shocks to banks and the state of their balance sheets at the time. To that we now turn, based on crisis experience. Any counter-factual empirical exercise is subject to huge caveats. Nonetheless, it is revealing to consider experience during the crisis to see what this revealed about the risks banks faced and how different regulatory constraints might have handled them. For example, recent research has found the leverage ratio and structural funding metrics performed well in predicting bank failure during the crisis (Huang and Ratnovski (2009), Demirguc-Kunt et al (2010), Bologna (2011), Arjani and Paulin (2013), Vazquez and Federico (2015)). And Lallour and Mio (2016) find that the NSFR had significant discriminatory power in identifying failing banks during the crisis, after controlling for banks’ solvency ratios. This line of research typically deploys regression approaches which weight together the information across different indicators.
Table 2: Assessment of the relative suitability of Basel III standards to address selected forms of risk  Risk Microprudential solvency risk – ‘true’ asset risk  Microprudential solvency risk – ‘unknown’ asset risk under Knightian uncertainty  First Best Mitigant RWCR: Requires loss absorbing capital to cover solvency risks. If risk can be measured and risk weights can be chosen appropriately, this allows for the greatest level of granularity. LR: Effective when risks are unknowable and cannot pinpoint particular asset classes of concern, especially in the face of limited historical data or fattailed loss distributions. Vulnerability to risk shifting arbitrage  RWCR: High degree of granularity reduces the scope for risk-shifting  Vulnerability to gaming  LR: Lack of granularity and degrees of freedom minimises gaming opportunities. Second Best Mitigant LR: Provides loss absorbing capacity but does not include any risk granularity by design. Less effective Mitigants LCR & NSFR: Neither ratio attempts to mitigate the risk of losses. RWCR: Provides loss absorbing capacity but may perform less well out-of-sample and vulnerable to model risk (IRB approach) or miscalibration of risk weights (standardised approach). LCR & NSFR: standardised assumptions mitigate some scope to shift risk but also allow some scope for distortion if weights are miscalibrated. LCR and NSFR: Small number of modelled assumptions offer some safeguard against gaming. LCR & NSFR: Neither ratio attempts to mitigate the risk of losses. LR: Greatest scope for distortion through risk shifting because of lack of risk sensitivity.