Francisco Javier Población García
- 24 February 2022
- WORKING PAPER SERIES - No. 2648Details
- Abstract
- The purpose of this paper is to investigate the main drivers of the change in the credit risk provisions at a portfolio level for the banks that have been subject of the 2018 EBA stress tests. Therefore, we perform a holistic review of the drivers of the three-year projections of credit losses. First, we define a model containing all the macroeconomic variables considered by the EBA methodological approach. By adding a three-dimension set of explanatory variables, entity-, banking sector- and portfolio-level aspects, we verify whether the published results show some kind of relation with these explanatory variables. Our results show that, although EBA variables explain most part of credit risk provisions, we obtain evidence about the role played by bank-level variables, banking sector features in each country, and the specific characteristics of the portfolio in explaining part of the provisions. Moreover, the results also indicate the existence of complementary/substitution effects of both bank- and portfolio-level variables with the characteristics of the banking sector when explaining credit risk provisions.
- JEL Code
- G20 : Financial Economics→Financial Institutions and Services→General
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
G28 : Financial Economics→Financial Institutions and Services→Government Policy and Regulation
- 19 December 2019
- WORKING PAPER SERIES - No. 2347Details
- Abstract
- The purpose of this paper is to compare the cyclical behavior of various credit impairment accounting regimes, namely IAS 39, IFRS 9 and US GAAP. We model the impact of credit impairments on the Profit and Loss (P&L) account under all three regimes. Our results suggest that although IFRS 9 is less procyclical than the previous regulation (IAS 39), it is more procyclical than US GAAP because it merely requests to provision the expected loss of one year under Stage 1 (initial category). Instead, since US GAAP prescribes that lifetime expected losses are fully provisioned at inception, the amount of new loans originated is negatively correlated with realized losses. This leads to relatively higher (lower) provisions during the upswing (downswing) phase of the financial cycle. Nevertheless, the lower procyclicality of US GAAP seems to come at cost of a large increase in provisions.
- JEL Code
- G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
G28 : Financial Economics→Financial Institutions and Services→Government Policy and Regulation
K20 : Law and Economics→Regulation and Business Law→General
- 10 July 2019
- WORKING PAPER SERIES - No. 2294Details
- Abstract
- We assess the effects of regulatory caps in the loan-to-value (LTV) ratio using agent-based models (ABMs). Our approach builds upon a straightforward ABM where we model the interactions of sellers, buyers and banks within a computational framework that enables the application of LTV caps. The results are first presented using simulated data and then we calibrate the probability distributions based on actual European data from the HFCS survey. The results suggest that this approach can be viewed as a useful alternative to the existing analytical frameworks for assessing the impact of macroprudential measures, mainly due to the very few assumptions the method relies upon and the ability to easily incorporate additional and more complex features related to the behavioral response of borrowers to such measures.
- JEL Code
- D14 : Microeconomics→Household Behavior and Family Economics→Household Saving; Personal Finance
D31 : Microeconomics→Distribution→Personal Income, Wealth, and Their Distributions
E50 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→General
R21 : Urban, Rural, Regional, Real Estate, and Transportation Economics→Household Analysis→Housing Demand
- 2 October 2018
- WORKING PAPER SERIES - No. 2181Details
- Abstract
- We develop a structural model for valuing bank balance sheet components such as the equity and debt value, the value for the government when the bank is operated by private shareholders including the present value of a possible future bailout, the bailout value incurred by the government following the abandonment of the private shareholders, and, moreover, some price and risk parameters, including the funding cost spread and the banks’ probability of default. The structural model implies an abandonment threshold, at which if total income drops below this threshold, private shareholders abandon the bank. In this case, the shareholders lose part (or all) of the capital that they hold in the bank, the creditors lose part or all of their debt, and the government receives a portion (or all) of the capital and all of the debt that is not recovered by creditors. Hence, we assume that part of the capital can be lost due to financial distress or to cover bankruptcy costs. We use the model framework to assess the impact of capital-based macro-prudential policy measures and focus in particular on assessing the difference that an assumed bail-in as opposed to bail-out regime can make.
- JEL Code
- G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
G28 : Financial Economics→Financial Institutions and Services→Government Policy and Regulation
H81 : Public Economics→Miscellaneous Issues→Governmental Loans, Loan Guarantees, Credits, Grants, Bailouts
- 13 November 2017
- WORKING PAPER SERIES - No. 2110Details
- Abstract
- In this paper, we construct a structural model to determine the costs of a bank rescue considering bail-outs and bail-ins. In our model, a government assumes the equity stake under unlimited liability upon abandonment of the original equity holders. The model determines an abandonment trigger such that if total income drops below this trigger, private shareholders abandon the bank. Given this trigger, the model also determines the bank rescue costs, the expected time to the bank rescue and the bank rescue probabilities. A static analysis of our model produces several empirically testable hypotheses. The model was explored in a sample of southern European countries considering alternative assumptions regarding parameter estimates and the behavior of operational costs. The model results regarding the rescue costs are reasonable, but the model also predicts bank rescues, estimates equity values, performs welfare analyses and estimates the impact of different macro- and micro-prudential policies. The empirical exercise we present, highlights the importance of the assumptions made regarding the behavior of the operational costs by showing dramatic differences in results in a sample of countries that otherwise appear to share important cultural and geographical proximities.
- JEL Code
- G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
G28 : Financial Economics→Financial Institutions and Services→Government Policy and Regulation
H81 : Public Economics→Miscellaneous Issues→Governmental Loans, Loan Guarantees, Credits, Grants, Bailouts
- 5 February 2016
- WORKING PAPER SERIES - No. 1881Details
- Abstract
- We develop an integrated micro-macro model framework that is based on household survey data for a subset of the EU countries that the Household Finance and Consumption Survey (HFCS) contains. The model can be used for conducting scenario and sensitivity analyses with regard to the factors that drive households' income and expenses as well as their asset values and hence the structure of their balance sheet. Moreover, we use it for the purpose of assessing the efficacy of borrower-based macroprudential instruments, namely loan-to-value (LTV) ratio and debt service to income (DSTI) ratio caps. The simulation results from the model can be attached to bank balance sheets and their risk parameters to derive the impact of the policy measures on their capital position. The model framework also allows quantifying the macroeconomic feedback effects that would result from the policy-induced reduction of demand for mortgage loans. The model allows answering the question as to which of the two measures
- JEL Code
- C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies
G18 : Financial Economics→General Financial Markets→Government Policy and Regulation - Network
- Household Finance and Consumption Network (HFCN)
- 7 September 2015
- WORKING PAPER SERIES - No. 1845Details
- Abstract
- The purpose of this paper is to promote the use of Bayesian model averaging for the design of satellite models that financial institutions employ for stress testing. Banks employing
- JEL Code
- C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
C22 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models &bull Diffusion Processes
C51 : Mathematical and Quantitative Methods→Econometric Modeling→Model Construction and Estimation
E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages