The generalized Vasicek credit risk model: A Machine Learning approach

Rubén García-Céspedes and Manuel Moreno

Finance Research Letters, 2022, vol. 47, issue PA

Abstract: This paper explores the ability of the Machine Learning (ML) techniques to calibrate models that replicate the outputs of the Vasicek (1987) credit risk model. In the general case, estimating the loss distribution in this model requires computationally demanding Monte Carlo simulations while the ML approach only requires an initial calibration process. For different granular or concentrated portfolios, our results show that using just two variables (the confidence level and a Gaussian copula-based loss distribution estimate), the tree-based models provide fast and accurate estimates of the real loss distribution.

Keywords: Credit risk; Machine learning; Monte Carlo simulation; Vasicek (1987) model (search for similar items in EconPapers)
JEL-codes: C14 C45 C63 G21 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:finlet:v:47:y:2022:i:pa:s1544612321005705

DOI: 10.1016/j.frl.2021.102669

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