BANKING
Quantitative Banking
| Coordinatore | IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE
Organization address
address: SOUTH KENSINGTON CAMPUS EXHIBITION ROAD contact info |
| Nazionalità Coordinatore | United Kingdom [UK] |
| Totale costo | 100˙000 € |
| EC contributo | 100˙000 € |
| Programma | FP7-PEOPLE
Specific programme "People" implementing the Seventh Framework Programme of the European Community for research, technological development and demonstration activities (2007 to 2013) |
| Code Call | FP7-PEOPLE-2013-CIG |
| Funding Scheme | MC-CIG |
| Anno di inizio | 2014 |
| Periodo (anno-mese-giorno) | 2014-04-01 - 2018-03-31 |
Partecipanti
| # | ||||
|---|---|---|---|---|
| 1 |
IMPERIAL COLLEGE OF SCIENCE, TECHNOLOGY AND MEDICINE
Organization address
address: SOUTH KENSINGTON CAMPUS EXHIBITION ROAD contact info |
UK (LONDON) | coordinator | 100˙000.00 |
Mappa
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Obiettivo del progetto (Objective)
'This is a quantitative study of banking behaviour in both partial and general equilibrium settings with incomplete markets. Despite the importance of banks and the possibility of banking defaults in a crisis, there exists little work studying bank behaviour in a quantitative setting. Using micro data from banks’ balance sheets, we establish stylized facts about individual bank behaviour. At the cross sectional level, more leveraged banks are more likely to default, larger banks (by asset size) are more leveraged, have lower equity as a share of deposits and rely more heavily on the federal funds market to finance new loans. At the time series (aggregate) level, problem loans rise in a recession and are negatively correlated with new loan growth, the aggregate default rate is strongly countercyclical, while banks face substantial idiosyncratic (background) risk in the form of liquidity shocks through deposit changes and problem loans. We propose to build different quantitative models to understand these facts. The bank faces realistic aggregate and idiosyncratic risks in the form of problem loan processes and deposit growth. At the same time the bank faces realistic regulatory constraints like a leverage constraint. The bank chooses whether to default or not. If the bank does not default, then the bank chooses the amount of new loans to make, the amount of liquid securities to hold and the amount of borrowing on the interbank market. The model is calibrated, solved and simulated to generate predictions close to its empirical counterparts. Interesting counterfactuals given this setup include increasing core tier-I capital ratios from 8% to 9%. Another interesting counterfactual involves new loans funding when the interbank market freezes. The modelling perspective opens up a substantial number of issues that can be addressed in a quantitative setting: comparing different recapitalization methods, optimal regulatory responses and optimal central bank policy'