On the optimal design of operational risk data consortiums

To manage operational risk banks increasingly use data coming from data consortia formed by peer institutions. Although existing data consor- tia seem to work appropriately, it is worth examining why banks report properly (that is, thoroughly and truthfully), since in several countries where new data consortia are planned to be set up, there are fears that banks may choose to report untruthfully or hide information (what we call misreporting). We show that if misreporting cannot be detected, then even in an inÖnitely repeated setup the game has multiple equilibria, so proper reporting is not the unique outcome. Then we analyze two types of sanctions. When the punishment is non-monetary (e.g. exclusion from the consortium for a given number of periods), then for some parameter values even the harshest punishment cannot bring about proper reporting as the unique outcome. Nonetheless, a numerical example suggests that by designing adequately the data consortium, proper reporting can be ad- vanced, without overly compromising anonymity. When a monetary Öne is imposed on misreporting banks, then a su¢ ciently sever punishment results in proper reporting, even if anonymity is maintained in the limit

Sequential decisions in the Diamond-Dybvig banking model

Abstract We study the Diamond-Dybvig model of financial intermediation (Diamond, D., Dybvig, P., 1983. Bank runs, deposit insurance and liquidity. Journal of Political Economy 91 (3), 401–419.) under the assumption that depositors have information about previous decisions. Depositors decide sequentially whether to withdraw their funds or continue holding them in the bank. If depositors observe the history of all previous decisions, we show that there are no bank runs in equilibrium independently of whether the realized type vector selected by nature is of perfect or imperfect information. Our result is robust to several extensions

Correlated observations, the law of small numbers and bank runs

Empirical descriptions and studies suggest that generally depositors observe a sample of previous decisions before deciding if to keep their funds deposited or to withdraw them. These observed decisions may exhibit different degrees of correlation across depositors. In our model depositors decide sequentially and are assumed to follow the law of small numbers in the sense that they believe that a bank run is underway if the number of observed withdrawals in their sample is large. Theoretically, with highly correlated samples and infinite depositors runs occur with certainty, while with random samples it needs not be the case, as for many parameter settings the likelihood of bank runs is zero. We investigate the intermediate cases and find that i) decreasing the correlation and ii) increasing the sample size reduces the likelihood of bank runs, ceteris paribus. Interestingly, the multiplicity of equilibria, a feature of the canonical Diamond-Dybvig model that we use also, disappears almost completely in our setup. Our results have relevant policy implications