How Risky Are Banks' Risk Weighted Assets? Evidence From the Financial Crisis

Das, Sonali; Sy, Amadou

doi:10.5089/9781463933791.001

Cited by 53 publications

(26 citation statements)

References 13 publications

(5 reference statements)

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“…Das and Sy (2012) Our paper differs from this literature. First, we do not simply describe differences across banks but use economic theory to formulate hypotheses that are tested on the basis of econometric models.…”

Section: Introductioncontrasting

confidence: 50%

Why Do Banks Optimize Risk Weights? The Relevance of the Cost of Equity Capital

Beltratti

Paladino

2013

SSRN Journal

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Banks use internal models to optimize risk weights and better account for the specific risk of each asset class. As the choice of a set of risk weights directly amounts to affecting the regulatory capital ratio, economic theory suggests that banks should optimize their risk weights also with respect to the cost and benefit of holding equity capital. Banks with a higher cost of capital, and banks with better growth opportunities, should be more aggressive in reducing risk weights. We consider a large panel of international banks and find that, after controlling for a number of bank and country characteristics, banks do respond to the cost and benefit of holding capital when selecting their average risk weights. We also find that banks that are more aggressive in terms of such optimization have a subsequent lower return on equity and are more likely to have raised capital during the credit crisis.3

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Section: Introductioncontrasting

confidence: 50%

Why Do Banks Optimize Risk Weights? The Relevance of the Cost of Equity Capital

Beltratti

Paladino

2013

SSRN Journal

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“…whether the concept of Basel's RWA really has the ability to represent a bank's true contribution to risk in the financial sector. See Das and Sy (2012) on that issue. Our analysis just follows the theoretical argument of Lautenschläger (2013) …”

Section: Limited Bank Leverage (Lr)mentioning

confidence: 99%

The impact of Basel III on financial (in)stability: an agent-based credit network approach

Krug

Lengnick

Wohltmann

2015

Quantitative Finance

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in AbstractThe Basel III accord reacts to the events of the recent financial crisis with a combination of revised micro-and new macroprudential regulatory instruments to address various dimensions of systemic risk. This approach of cumulating requirements bears the risk of individual measures negating or even conflicting with each other which might lessen their desired effects on financial stability. We provide an analysis of the impact of Basel III's main components on financial stability in a stock-flow consistent (SFC) agent-based computational economic (ACE) model. We find that the positive joint impact of the microprudential instruments is considerably larger than the sum of the individual contributions to stability, i.e. the standalone impacts are non-additive. However, except for the buffers, the macroprudential overlay's impact is either marginal or even destabilizing. Despite its simplicity, the leverage ratio performs poorly especially when associated drawbacks are explicitly taken into account. Surcharges on SIBs seem to rather contribute to financial regulations complexity than to the resilience of the system.JEL classification: G01, G28, E40, C63

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“…10 See also Das and Sy (2012), Hagendorff and Vallascas (2013), and Mariathasan and Merrouche (2014). 11 See, e.g., Glaeser and Shleifer (2001), Hellwig (2010), Hoenig (2010), Haldane (2011), Haldane (2012, Admati and The rest of the paper is organized as follows.…”

Section: Introductionmentioning

confidence: 99%

The Limits of Model-Based Regulation

2014

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In this paper, we investigate how the introduction of complex, model-based capital regulation affected credit risk of financial institutions. Model-based regulation was meant to enhance the stability of the financial sector by making capital charges more sensitive to risk. Exploiting the staggered introduction of the model-based approach in Germany and the richness of our loan-level data set, we show that (1) internal risk estimates employed for regulatory purposes systematically underpredict actual default rates by 0.5 to 1 percentage points; (2) both default rates and loss rates are higher for loans that were originated under the model-based approach, while corresponding risk-weights are significantly lower; and (3) interest rates are higher for loans originated under the model-based approach, suggesting that banks were aware of the higher risk associated with these loans and priced them accordingly. Further, we document that large banks benefited from the reform as they experienced a reduction in capital charges and consequently expanded their lending at the expense of smaller banks that did not introduce the model-based approach. Counter to the stated objectives, the introduction of complex regulation adversely affected the credit risk of financial institutions. Overall, our results highlight the pitfalls of complex regulation and suggest that simpler rules may increase the efficacy of financial regulation.

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How Risky Are Banks' Risk Weighted Assets? Evidence From the Financial Crisis

Cited by 53 publications

References 13 publications

Why Do Banks Optimize Risk Weights? The Relevance of the Cost of Equity Capital

Why Do Banks Optimize Risk Weights? The Relevance of the Cost of Equity Capital

The impact of Basel III on financial (in)stability: an agent-based credit network approach

The Limits of Model-Based Regulation

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