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Heterogeneous credit portfolios and the dynamics of the aggregate losses

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  • Dai Pra, Paolo
  • Tolotti, Marco

Abstract

We study the impact of contagion in a network of firms facing credit risk. We describe an intensity based model where the homogeneity assumption is broken by introducing a random environment that makes it possible to take into account the idiosyncratic characteristics of the firms. We shall see that our model goes behind the identification of groups of firms that can be considered basically exchangeable. Despite this heterogeneity assumption our model has the advantage of being totally tractable. The aim is to quantify the losses that a bank may suffer in a large credit portfolio. Relying on a large deviation principle on the trajectory space of the process, we state a suitable law of large numbers and a central limit theorem useful for studying large portfolio losses. Simulation results are provided as well as applications to portfolio loss distribution analysis.

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Bibliographic Info

Article provided by Elsevier in its journal Stochastic Processes and their Applications.

Volume (Year): 119 (2009)
Issue (Month): 9 (September)
Pages: 2913-2944

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Handle: RePEc:eee:spapps:v:119:y:2009:i:9:p:2913-2944

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Keywords: Central limit theorems in Banach spaces Credit contagion Intensity based models Large deviations Large portfolio losses Random environment;

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References

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  1. Crouhy, Michel & Galai, Dan & Mark, Robert, 2000. "A comparative analysis of current credit risk models," Journal of Banking & Finance, Elsevier, vol. 24(1-2), pages 59-117, January.
  2. Amir Dembo & Jean-Deominique Deuschel & Darrell Duffie, 2002. "Large Portfolio Losses," NBER Working Papers 9177, National Bureau of Economic Research, Inc.
  3. Stefan Weber & Kay Giesecke, 2003. "Credit Contagion and Aggregate Losses," Computing in Economics and Finance 2003 246, Society for Computational Economics.
  4. Paolo Dai Pra & Wolfgang J. Runggaldier & Elena Sartori & Marco Tolotti, 2007. "Large portfolio losses: A dynamic contagion model," Papers 0704.1348, arXiv.org, revised Mar 2009.
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Cited by:
  1. Cinzia Colapinto & Elena Sartori & Marco Tolotti, 2012. "A two-stage model for diffusion of innovations," Working Papers 16, Department of Management, Università Ca' Foscari Venezia.
  2. Konstantinos Spiliopoulos, 2014. "Systemic Risk and Default Clustering for Large Financial Systems," Papers 1402.5352, arXiv.org.
  3. Kay Giesecke & Konstantinos Spiliopoulos & Richard B. Sowers & Justin A. Sirignano, 2011. "Large Portfolio Asymptotics for Loss From Default," Papers 1109.1272, arXiv.org, revised Oct 2013.
  4. Kay Giesecke & Konstantinos Spiliopoulos & Richard B. Sowers, 2011. "Default clustering in large portfolios: Typical events," Papers 1104.1773, arXiv.org, revised Feb 2013.
  5. Colapinto, Cinzia & Sartori, Elena & Tolotti, Marco, 2014. "Awareness, persuasion, and adoption: Enriching the Bass model," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 395(C), pages 1-10.
  6. Konstantinos Spiliopoulos & Richard B. Sowers, 2013. "Default Clustering in Large Pools: Large Deviations," Papers 1311.0498, arXiv.org.
  7. Konstantinos Spiliopoulos & Justin A. Sirignano & Kay Giesecke, 2013. "Fluctuation Analysis for the Loss From Default," Papers 1304.1420, arXiv.org, revised Oct 2013.

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