Gutierrez Girault, Matias Alfredo (2008): Modeling extreme but plausible losses for credit risk: a stress testing framework for the Argentine Financial System.
Download (192kB) | Preview
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.
|Item Type:||MPRA Paper|
|Original Title:||Modeling extreme but plausible losses for credit risk: a stress testing framework for the Argentine Financial System|
|Keywords:||stress test; credit risk; dynamic panel data; Monte Carlo|
|Subjects:||F - International Economics > F3 - International Finance > F37 - International Finance Forecasting and Simulation: Models and Applications
G - Financial Economics > G2 - Financial Institutions and Services > G28 - Government Policy and Regulation
E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E37 - Forecasting and Simulation: Models and Applications
G - Financial Economics > G2 - Financial Institutions and Services > G21 - Banks; Depository Institutions; Micro Finance Institutions; Mortgages
|Depositing User:||Matias Gutierrez Girault|
|Date Deposited:||22. Jul 2009 05:18|
|Last Modified:||12. Feb 2013 10:55|
Arellano, M. and S. Bond (1991). Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations, Review of Economic Studies, 58, 277-297.
Anderson, T.W. and C. Hsiao (1982). Formulation and Estimation of Dynamic Models using Panel Data, Journal of Econometrics, 18, 47-82.
Basel Committee on Banking Supervision (2006). International Convergence of Capital Measurement and Capital Standards: A Revised Framework - Comprehensive Version, Bank for International Settlements.
Behr, A. (2003). A comparison of dynamic panel data estimators: Monte Carlo evidence and an application to the investment function. Discussion Paper 05/03, Deutsche Bundesbank.
Blundell, R. and S. Bond (1998). Initial Conditions and Moment Restrictions in Dynamic Panel Data Models, Journal of Econometrics, 87, 115-143.
Bruno, G.S.F. (2004). Approximating the Bias of the LSDV Estimator for Dynamic Unbalanced Panel Data Models, Universita Bocconi, Instituto di Economia Politica.
Committee on the Global Financial System (2001). A survey of stress tests and current practice at major financial institutions, Bank for International Settlements.
Committee on the Global Financial System (2005). Stress testing at major financial institutions: survey results and practice, Bank for International Settlements.
Gutierrez Girault, M. (2007). Non – Parametric Estimation of Conditional and Unconditional Loan Portfolio Loss Distributions with Public Credit Registry Data, Technical Document, Central Bank of Argentina.
Hayakawa, H. (2005). Small Sample Bias Properties of the System GMM Estimator in Dynamic Panel Data Models, Discussion Paper Series N°82, Institute of Economic Research, Hitotsubashi University.
Jorion, P. (2001). Value at Risk, McGraw-Hill.
Judson, R.A. and A.L. Owen (1996). Estimating Dynamic Panel Data Models: A Practical Guide for Macroeconomists. Federal Reserve Board of Governors.
Kiviet, J.F. (1995). On bias, inconsistency and efficiency of various estimators in dynamic panel data models, Journal of Econometrics, 68, 53-78.
Marcucci, J. and M. Quagliariello (2005). Is Bank Portfolio Procyclical? Evidence from Italy using a Vector Autoregression. Discussion Paper in Economics 2005/09, The University of York.
Monetary Authority of Singapore (2003). Technical Paper on Credit Stress-Testing, MAS Information Paper 01-2003.
Nickell, S. (1981). Biases in Dynamic Models with Fixed Effects, Econometrica, Vol. 49, No. 6. pp. 1417-1426.
Superintendence of Exchange and Financial Institutions (2006), Survey of stress tests, Financial System Analysis Department, Market Risk Division.