Modeling Path-Dependent State Transition by a Recurrent Neural Network

Bill Huajian Yang

MPRA Paper from University Library of Munich, Germany

Abstract: Rating transition models are widely used for credit risk evaluation. It is not uncommon that a time-homogeneous Markov rating migration model deteriorates quickly after projecting repeatedly for a few periods. This is because the time-homogeneous Markov condition is generally not satisfied. For a credit portfolio, rating transition is usually path dependent. In this paper, we propose a recurrent neural network (RNN) model for modeling path-dependent rating migration. An RNN is a type of artificial neural networks where connections between nodes form a directed graph along a temporal sequence. There are neurons for input and output at each time-period. The model is informed by the past behaviours for a loan along the path. Information learned from previous periods propagates to future periods. Experiments show this RNN model is robust.

Keywords: Path-dependent; rating transition; recurrent neural network; deep learning; Markov property; time-homogeneity (search for similar items in EconPapers)
JEL-codes: C13 C18 C45 C51 C58 G12 G17 G32 G33 M3 (search for similar items in EconPapers)
Date: 2022-08-18, Revised 2022-07-18
New Economics Papers: this item is included in nep-big, nep-cmp and nep-net
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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:114188

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