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Modeling extreme but plausible losses for credit risk: a stress testing framework for the Argentine Financial System

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  • Gutierrez Girault, Matias Alfredo

Abstract

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.

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Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 16378.

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Date of creation: Jun 2008
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Handle: RePEc:pra:mprapa:16378

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Keywords: stress test; credit risk; dynamic panel data; Monte Carlo;

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  1. Arellano, Manuel & Bond, Stephen, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Wiley Blackwell, vol. 58(2), pages 277-97, April.
  2. 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.
  3. 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.
  4. Richard Blundell & Steve Bond, 1995. "Initial conditions and moment restrictions in dynamic panel data models," IFS Working Papers W95/17, Institute for Fiscal Studies.
  5. Juri Marcucci & Mario Quagliariello, . "Is Bank Portfolio Riskiness Procyclical? Evidence from Italy using a Vector Autoregression," Discussion Papers 05/09, Department of Economics, University of York.
  6. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-26, November.
  7. 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.
  8. Ruth A. 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.).
  9. 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.
  10. 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.
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