Rating transition models are widely used for credit risk evaluation. It is not uncommon that a time-homogeneous Markov rating migration model will deteriorate quickly after projecting repeatedly for a few periods. This is because the time-homogeneous Markov condition is generally not satisfied. For a credit portfolio, the 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 network 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 behaviors for a loan along the path. Information learned from previous periods propagates to future periods. The experiments show that this RNN model is robust.
Citation: Bill Huajian Yang. Modeling path-dependent state transitions by a recurrent neural network[J]. Big Data and Information Analytics, 2022, 7: 1-12. doi: 10.3934/bdia.2022001
Rating transition models are widely used for credit risk evaluation. It is not uncommon that a time-homogeneous Markov rating migration model will deteriorate quickly after projecting repeatedly for a few periods. This is because the time-homogeneous Markov condition is generally not satisfied. For a credit portfolio, the 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 network 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 behaviors for a loan along the path. Information learned from previous periods propagates to future periods. The experiments show that this RNN model is robust.
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Bill Huajian Yang. Modeling path-dependent state transitions by a recurrent neural network[J]. Big Data and Information Analytics, 2022, 7: 1-12. doi: 10.3934/bdia.2022001
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