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A Deep Learning Approach to Estimate Forward Default Intensities

Author

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  • Marc-Aurèle Divernois

    (EPFL; 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.

Suggested Citation

  • Marc-Aurèle Divernois, 2020. "A Deep Learning Approach to Estimate Forward Default Intensities," Swiss Finance Institute Research Paper Series 20-79, Swiss Finance Institute.
    • Handle: RePEc:chf:rpseri:rp2079
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    More about this item

    Keywords

    Bankruptcy; Credit Risk; Default; Machine Learning; Neural Networks; Doubly Stochastic; Forward Poisson Intensities;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • G34 - Financial Economics - - Corporate Finance and Governance - - - Mergers; Acquisitions; Restructuring; Corporate Governance

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