Correlated Risks vs Contagion in Stochastic Transition Models

Patrick Gagliardini () and Christian Gourieroux

No 2012-07, Working Papers from Center for Research in Economics and Statistics

Abstract: There is a growing literature on the possibility to identify correlation and contagion in qualitative risk analysis. Our paper considers this question by means of a model describing the joint dynamics of a set of individual binary processes. The two admissible values correspond to bad and good risk states of an individual. The risk correlation and its time dependence are captured by introducing a dynamic frailty, whereas the contagion passes through the effect of the lagged number of individuals in the bad risk state. We study carefully the dynamic properties of the joint process. Then, we focus on the limiting case of large populations (portfolios) and reconcile the microscopic and macroscopic dynamic views of the risk. The difficulty to identify in finite sample risk correlation and contagion is illustrated by means of Monte-Carlo simulations

Keywords: Risk Dependence; Frailty; Systematic Risk; Contagion; Count Process; INAR Model; Compound Autoregressive Process; Affine Model; Credit Risk; Granularity Adjustment; Stochastic Intensity. (search for similar items in EconPapers)
JEL-codes: C23 G12 (search for similar items in EconPapers)
Pages: 62
Date: 2012-03
New Economics Papers: this item is included in nep-ets and nep-ore
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Citations: View citations in EconPapers (1)

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Journal Article: Correlated risks vs contagion in stochastic transition models (2013)
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