Modeling extreme but plausible losses for credit risk: a stress testing framework for the Argentine Financial System
While not being widespread, stress tests of credit risk are not new in the Argentine financial system, neither for financial intermediaries nor for the Central Bank. However, they are more often based on rule-of-thumb approaches than on systematic, model based methodologies. The objective of this paper is to fill this gap. With a database that covers the 1994-2006 period we implement a three staged approach. First, we use bank balance sheet data to estimate a dynamic panel data model, with different statistical methodologies, to explain bank losses for credit risk with bank-specific and macroeconomic variables. In a second step, the macroeconomic drivers of bank losses, real GDP growth and cost of short term credit, are modeled with a Vector Autoregression (VAR). The VAR shows the effect of the variables (i.e. risk factors) that we find dominate the domestic business cycle: the price of commodities, the sovereign risk and the federal funds rate. Finally, we use this toolkit to perform deterministic and stochastic scenario analysis. In the first case we use the behavior of the risk factors during the crisis of 1995 (Tequila contagion) and 2001 (Currency Board collapse), and we implement a subjective scenario as well. The stochastic scenarios are performed by Monte Carlo with two alternative methodologies: a non-parametric bootstrapping approach and drawing repeatedly from a multivariate normal distribution. When comparing the estimated unexpected losses to available capital, we find that currently the Argentine financial system is adequately capitalized to absorb the higher losses that would take place in a stress situation.
|Date of creation:||Jun 2008|
|Date of revision:|
|Contact details of provider:|| Postal: |
Web page: https://mpra.ub.uni-muenchen.de
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Anderson, T. W. & Hsiao, Cheng, 1982. "Formulation and estimation of dynamic models using panel data," Journal of Econometrics, Elsevier, vol. 18(1), pages 47-82, January.
- Kazuhiko Hayakawa, 2005.
"Small Sample Bias Propreties of the System GMM Estimator in Dynamic Panel Data Models,"
Hi-Stat Discussion Paper Series
d05-82, Institute of Economic Research, Hitotsubashi University.
- Hayakawa, Kazuhiko, 2007. "Small sample bias properties of the system GMM estimator in dynamic panel data models," Economics Letters, Elsevier, vol. 95(1), pages 32-38, April.
- repec:oup:restud:v:58:y:1991:i:2:p:277-97 is not listed on IDEAS
- Giovanni S.F. Bruno, 2004.
"Approximating the Bias of the LSDV Estimator for Dynamic Unbalanced Panel Data Models,"
KITeS Working Papers
159, KITeS, Centre for Knowledge, Internationalization and Technology Studies, Universita' Bocconi, Milano, Italy, revised Jul 2004.
- Bruno, Giovanni S.F., 2005. "Approximating the bias of the LSDV estimator for dynamic unbalanced panel data models," Economics Letters, Elsevier, vol. 87(3), pages 361-366, June.
- Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-26, November.
- Ruth Judson & Ann L. Owen, 1997. "Estimating dynamic panel data models: a practical guide for macroeconomists," Finance and Economics Discussion Series 1997-3, Board of Governors of the Federal Reserve System (U.S.).
- Juri Marcucci & Mario Quagliariello, .
"Is Bank Portfolio Riskiness Procyclical? Evidence from Italy using a Vector Autoregression,"
05/09, Department of Economics, University of York.
- Marcucci, Juri & Quagliariello, Mario, 2008. "Is bank portfolio riskiness procyclical: Evidence from Italy using a vector autoregression," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 18(1), pages 46-63, February.
- Kiviet, Jan F., 1995.
"On bias, inconsistency, and efficiency of various estimators in dynamic panel data models,"
Journal of Econometrics,
Elsevier, vol. 68(1), pages 53-78, July.
- Tom Doan, . "LSDVC: RATS procedure to estimate a dynamic FE model with correction for bias," Statistical Software Components RTS00111, Boston College Department of Economics.
- R Blundell & Steven Bond, .
"Initial conditions and moment restrictions in dynamic panel data model,"
W14&104., Economics Group, Nuffield College, University of Oxford.
- Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August.
- Richard Blundell & Steve Bond, 1995. "Initial conditions and moment restrictions in dynamic panel data models," IFS Working Papers W95/17, Institute for Fiscal Studies.
- Blundell, R. & Bond, S., 1995. "Initial Conditions and Moment Restrictions in Dynamic Panel Data Models," Economics Papers 104, Economics Group, Nuffield College, University of Oxford.
- Behr, Andreas, 2003. "A comparison of dynamic panel data estimators: Monte Carlo evidence and an application to the investment function," Discussion Paper Series 1: Economic Studies 2003,05, Deutsche Bundesbank, Research Centre.
- Gutierrez Girault, Matias, 2006. "Non – parametric estimation of conditional and unconditional loan portfolio loss distributions with public credit registry data," MPRA Paper 9798, University Library of Munich, Germany, revised Jun 2007.
When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:16378. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Ekkehart Schlicht)
If references are entirely missing, you can add them using this form.