Default clustering in large portfolios: Typical events.

By Kay Giesecke, Konstantinos Spiliopoulos, Richard B. Sowers. 2011

86 citations recorded by the source corpus.

We develop a dynamic point process model of correlated default timing in a portfolio of firms, and analyze typical default profiles in the limit as the size of the pool grows. In our model, a firm defaults at a stochastic intensity that is influenced by an idiosyncratic risk process, a systematic risk process common to all firms, and past defaults. We prove a law of large numbers for the default rate in the pool, which describes the "typical" behavior of defaults.

Subfields: Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/f6caadc59e942693515fa08e9d90e80607e2faa7

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In Management Science and Operations Research

Citations in the corpus

Mean field analysis of neural networks: A central limit theorem (2018, methodology): “all these issues for one-layer neural network models, rigorously establishing and characterizing the ﬂuctuations limit. Weak convergence analysis has been widely used in other ﬁelds (for example, see [14], [15], [5], [6], [7], [4], and [17] for a non-exhaustive list). In fact, mean ﬁeld analysis has”
$N$-player games and mean-field games with absorption (2016, result): “[19], while other results on the loss from default are analyzed in the companion papers Spiliopoulos et al.”
Mean Field Analysis of Neural Networks: A Law of Large Numbers (2018, methodology): “ limit, and uniqueness of the limit point in the appropriate space. Our method of proof leverages on weak convergence analysis in an appropriate Skorokhod space for measure-valued processes, see also [1, 2]. Department of Industrial & Systems Engineering, University of Illinois at Urbana Champai”