A Deep Learning Approach to Estimate Forward Default Intensities

Marc-Aurèle Divernois
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Marc-Aurèle Divernois: EPFL; Swiss Finance Institute

No 20-79, Swiss Finance Institute Research Paper Series from Swiss Finance Institute

Abstract: This paper proposes a machine learning approach to estimate physical forward default intensities. Default probabilities are computed using artificial neural networks to estimate the intensities of the inhomogeneous Poisson processes governing default process. The major contribution to previous literature is to allow the estimation of non-linear forward intensities by using neural networks instead of classical maximum likelihood estimation. The model specification allows an easy replication of previous literature using linear assumption and shows the improvement that can be achieved.

Keywords: Bankruptcy; Credit Risk; Default; Machine Learning; Neural Networks; Doubly Stochastic; Forward Poisson Intensities (search for similar items in EconPapers)
JEL-codes: C22 C23 C53 C58 G33 G34 (search for similar items in EconPapers)
Pages: 39 pages
Date: 2020-07
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ecm, nep-ore and nep-rmg
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Persistent link: https://EconPapers.repec.org/RePEc:chf:rpseri:rp2079

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