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This study analyzes information production and trading behavior of banks with lending relationships. We combine trade-by-trade supervisory data and credit-registry data to examine banks' proprietary trading in borrower stocks around a large number of corporate events. We find that relationship banks build up positive (negative) trading positions in the two weeks before events with positive (negative) news, even when these events are unscheduled, and unwind positions shortly after the event. This trading pattern is more pronounced in situations when banks are likely to possess private information about their borrowers, and cannot be explained by specialized expertise in certain industries or certain firms. The results suggest that banks' lending relationships inform their trading and underscore the potential for conflicts of interest in universal banking, which have been a prominent concern in the regulatory debate for a long time. Our analysis illustrates how combining large data sets can uncover unusual trading patterns and enhance the supervision of financial institutions.
We study the impact of higher capital requirements on banks’ balance sheets and its transmission to the real economy. The 2011 EBA capital exercise provides an almost ideal quasi-natural experiment, which allows us to identify the effect of higher capital requirements using a difference-in-differences matching estimator. We find that treated banks increase their capital ratios not by raising their levels of equity, but by reducing their credit supply. We also show that this reduction in credit supply results in lower firm-, investment-, and sales growth for firms which obtain a larger share of their bank credit from the treated banks.
This paper explores how banks adjust their risk-based capital ratios and asset allocations following an exogenous shock to their asset quality caused by Hurricane Katrina in 2005. We find that independent banks based in the disaster areas increase their risk-based capital ratios after the hurricane, while those part of a bank holding company do not. The effect on independent banks mainly comes from the subgroup of high-capitalized banks. These banks increase their holdings in government securities and reduce loans to non-financial firms. Hence, banks that become more stable achieve this at the cost of reduced lending.
his paper distils three lessons for bank regulation from the experience of the 2009-12 euro-area financial crisis. First, it highlights the key role that sovereign debt exposures of banks have played in the feedback loop between bank and fiscal distress, and inquires how the regulation of banks’ sovereign exposures in the euro area should be changed to mitigate this feedback loop in the future. Second, it explores the relationship between the forbearance of non-performing loans by European banks and the tendency of EU regulators to rescue rather than resolving distressed banks, and asks to what extent the new regulatory framework of the euro-area “banking union” can be expected to mitigate excessive forbearance and facilitate resolution of insolvent banks. Finally, the paper highlights that capital requirements based on the ratio of Tier-1 capital to banks’ risk-weighted assets were massively gamed by large banks, which engaged in various forms of regulatory arbitrage to minimize their capital charges while expanding leverage. This argues in favor of relying on a set of simpler and more robust indicators to determine banks’ capital shortfall, such as book and market leverage ratios.
This paper tests whether an increase in insured deposits causes banks to become more risky. We use variation introduced by the U.S. Emergency Economic Stabilization Act in October 2008, which increased the deposit insurance coverage from $100,000 to $250,000 per depositor and bank. For some banks, the amount of insured deposits increased significantly; for others, it was a minor change. Our analysis shows that the more affected banks increase their investments in risky commercial real estate loans and become more risky relative to unaffected banks following the change. This effect is most distinct for affected banks that are low capitalized.
Under a new Basel capital accord, bank regulators might use quantitative measures when evaluating the eligibility of internal credit rating systems for the internal ratings based approach. Based on data from Deutsche Bundesbank and using a simulation approach, we find that it is possible to identify strongly inferior rating systems out-of time based on statistics that measure either the quality of ranking borrowers from good to bad, or the quality of individual default probability forecasts. Banks do not significantly improve system quality if they use credit scores instead of ratings, or logistic regression default probability estimates instead of historical data. Banks that are not able to discriminate between high- and low-risk borrowers increase their average capital requirements due to the concavity of the capital requirements function.
Evaluating the quality of credit portfolio risk models is an important issue for both banks and regulators. Lopez and Saidenberg (2000) suggest cross-sectional resampling techniques in order to make efficient use of available data. We show that their proposal disregards cross-sectional dependence in resampled portfolios, which renders standard statistical inference invalid. We proceed by suggesting the Berkowitz (1999) procedure, which relies on standard likelihood ratio tests performed on transformed default data. We simulate the power of this approach in various settings including one in which the test is extended to incorporate cross-sectional information. To compare the predictive ability of alternative models, we propose to use either Bonferroni bounds or the likelihood-ratio of the two models. Monte Carlo simulations show that a default history of ten years can be sufficient to resolve uncertainties currently present in credit risk modeling.
Evaluating the quality of credit portfolio risk models is an important question for both banks and regulators. Lopez and Saidenberg (2000) suggest cross-sectional resampling techniques in order to make efficient use of available data and to produce measures of forecast accuracy. We first show that their proposal disregards crosssectional dependence in simulated subportfolios, which renders standard statistical inference invalid. We proceed by suggesting another evaluation methodology which draws on the concept of likelihood ratio tests. Specifically, we compare the predictive quality of alternative models by comparing the probabilities that observed data have been generated by these models. The distribution of the test statistic can be derived through Monte Carlo simulation. To exploit differences in cross-sectional predictions of alternative models, the test can be based on a linear combination of subportfolio statistics. In the construction of the test, the weight of a subportfolio depends on the difference in the loss distributions which alternative models predict for this particular portfolio. This makes efficient use of the data, and reduces computational burden. Monte Carlo simulations suggest that the power of the tests is satisfactory.
JEL classification: G2; G28; C52