Final Report Summary - BANKING (Quantitative Banking)
The main goal of the research proposal was to construct usable quantitative models to analyze bank behavior both in the cross section and over the business cycle in the presence of different regulatory frameworks with regards to capital requirements. The goal was to understand the differential effects of different bank capital requirements on loan supply, bank leverage choices and bank defaults both in the cross section and over time.
The topic is very important because many changes in bank capital requirements have taken place but to date there is no unanimity on how to quantitatively evaluate the effect of these important policy changes. After the global financial crisis, wide calls have been made to require banks to hold higher equity buffers. These buffers can then be relied upon to smooth unforeseen bad loan contingencies and prevent bank defaults. But what should the right level of capital requirements be? And should this level be different whether risk-weighted or unweighted leverage requirements are being changed?
To address these questions we estimate a dynamic structural banking model to examine the interaction between risk-weighted capital adequacy and unweighted leverage requirements, their differential impact on bank lending, and equity buffer accumulation in excess of regulatory minima. Tighter risk-weighted capital requirements reduce loan supply and lead to an endogenous fall in bank profitability, reducing bank incentives to accumulate equity buffers and, therefore, increasing the incidence of bank failure. Tighter leverage requirements, on the other hand, increase lending, preserve bank charter value and incentives to accumulate equity buffers, therefore leading to lower bank failure rates. The results might seem counter intuitive at first in the sense that a higher capital requirement could, under certain conditions, lead to higher, rather than lower, bank defaults. Nevertheless, the economic logic is not surprising. Given background risks held constant (from bad bank loans or liquidity shocks, for example), a tighter capital requirement will make the bank safer only if the bank saves more than the extent to which the capital requirement is tightened. The paper shows that this does not always happen and therefore quantitative and empirically grounded models can help provide real time answers to such questions.
Progress and Results
Progress on the initial plan has been good during the grant period. In the first grant period the data were collected and the model numerically solved. In the second part of the period the model was estimated, a paper written and the paper has been presented in top international conferences like the Econometric Society conference in 2016, the NBER Summer Institute in July 2016, and the European Finance Association meeting in 2017. The paper from this project has been conditionally accepted in August 2018 in Financial Management for a special issue on financial regulation.
Potential Impact
Understanding quantitatively how to set optimal capital requirements using empirically relevant banking models remains an important research topic. The model we have developed provides one approach to address this issue. I am optimistic that the extensions can provide a useful contribution that can help guide policy makers and academic researchers on the right level of complexity that might be needed to make correct policy choices in real time and under stress scenario situations.
The topic is very important because many changes in bank capital requirements have taken place but to date there is no unanimity on how to quantitatively evaluate the effect of these important policy changes. After the global financial crisis, wide calls have been made to require banks to hold higher equity buffers. These buffers can then be relied upon to smooth unforeseen bad loan contingencies and prevent bank defaults. But what should the right level of capital requirements be? And should this level be different whether risk-weighted or unweighted leverage requirements are being changed?
To address these questions we estimate a dynamic structural banking model to examine the interaction between risk-weighted capital adequacy and unweighted leverage requirements, their differential impact on bank lending, and equity buffer accumulation in excess of regulatory minima. Tighter risk-weighted capital requirements reduce loan supply and lead to an endogenous fall in bank profitability, reducing bank incentives to accumulate equity buffers and, therefore, increasing the incidence of bank failure. Tighter leverage requirements, on the other hand, increase lending, preserve bank charter value and incentives to accumulate equity buffers, therefore leading to lower bank failure rates. The results might seem counter intuitive at first in the sense that a higher capital requirement could, under certain conditions, lead to higher, rather than lower, bank defaults. Nevertheless, the economic logic is not surprising. Given background risks held constant (from bad bank loans or liquidity shocks, for example), a tighter capital requirement will make the bank safer only if the bank saves more than the extent to which the capital requirement is tightened. The paper shows that this does not always happen and therefore quantitative and empirically grounded models can help provide real time answers to such questions.
Progress and Results
Progress on the initial plan has been good during the grant period. In the first grant period the data were collected and the model numerically solved. In the second part of the period the model was estimated, a paper written and the paper has been presented in top international conferences like the Econometric Society conference in 2016, the NBER Summer Institute in July 2016, and the European Finance Association meeting in 2017. The paper from this project has been conditionally accepted in August 2018 in Financial Management for a special issue on financial regulation.
Potential Impact
Understanding quantitatively how to set optimal capital requirements using empirically relevant banking models remains an important research topic. The model we have developed provides one approach to address this issue. I am optimistic that the extensions can provide a useful contribution that can help guide policy makers and academic researchers on the right level of complexity that might be needed to make correct policy choices in real time and under stress scenario situations.
