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Modeling Path-Dependent State Transition by a Recurrent Neural Network

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  • Yang, Bill Huajian

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.

Suggested Citation

  • Yang, Bill Huajian, 2022. "Modeling Path-Dependent State Transition by a Recurrent Neural Network," MPRA Paper 114188, University Library of Munich, Germany, revised 18 Jul 2022.
    • Handle: RePEc:pra:mprapa:114188
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    References listed on IDEAS

    as
    1. Yang, Bill Huajian, 2017. "Forward Ordinal Probability Models for Point-in-Time Probability of Default Term Structure," MPRA Paper 79934, University Library of Munich, Germany.
    2. Emilio Russo, 2020. "A Discrete-Time Approach to Evaluate Path-Dependent Derivatives in a Regime-Switching Risk Model," Risks, MDPI, vol. 8(1), pages 1-22, January.
    3. Kiefer, Nicholas M. & Larson, C. Erik, 2007. "A simulation estimator for testing the time homogeneity of credit rating transitions," Journal of Empirical Finance, Elsevier, vol. 14(5), pages 818-835, December.
    4. G. dos Reis & M. Pfeuffer & G. Smith, 2020. "Capturing model risk and rating momentum in the estimation of probabilities of default and credit rating migrations," Quantitative Finance, Taylor & Francis Journals, vol. 20(7), pages 1069-1083, July.
    5. Yang, Bill Huajian & Du, Zunwei, 2016. "Rating Transition Probability Models and CCAR Stress Testing: Methodologies and implementations," MPRA Paper 76270, University Library of Munich, Germany.
    6. Marius Pfeuffer & Goncalo dos Reis & Greig smith, 2018. "Capturing Model Risk and Rating Momentum in the Estimation of Probabilities of Default and Credit Rating Migrations," Papers 1809.09889, arXiv.org, revised Feb 2020.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Path-dependent; rating transition; recurrent neural network; deep learning; Markov property; time-homogeneity;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • M3 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising

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