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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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Author Info
Gutierrez Girault, Matias Alfredo

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

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Find related papers by JEL classification:
F37 - International Economics - - International Finance - - - International Finance Forecasting and Simulation
G28 - Financial Economics - - Financial Institutions and Services - - - Government Policy and Regulation
E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation
G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Mortgages

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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, Blackwell Publishing, vol. 58(2), pages 277-97, April. [Downloadable!] (restricted)
  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. [Downloadable!]
  3. Giovanni S.F. Bruno, 2004. "Approximating the Bias of the LSDV Estimator for Dynamic Unbalanced Panel Data Models," CESPRI Working Papers 159, CESPRI, Centre for Research on Innovation and Internationalisation, Universita' Bocconi, Milano, Italy, revised Jul 2004. [Downloadable!]
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  4. 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. [Downloadable!] (restricted)
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  5. Nickell, Stephen J, 1981. "Biases in Dynamic Models with Fixed Effects," Econometrica, Econometric Society, vol. 49(6), pages 1417-26, November. [Downloadable!] (restricted)
  6. 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. [Downloadable!] (restricted)
  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. [Downloadable!]
  8. 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. [Downloadable!] (restricted)
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  9. 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. [Downloadable!] (restricted)
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