Applying Machine Learning for Troubleshooting Credit Exposure and xVA Profiles

Zhu, Shengyao; Chan, Justin W.; Bright, Damien

doi:10.2139/ssrn.3404863

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2021

2022

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Cited by 2 publications

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“…Hung et al (2020) [23] furthermore show that ANNs have been extensively applied to stock market prediction, stock trading, portfolio management, exchange rate prediction, macroeconomic prediction and credit and default risk. Zhu et al (2019) [42] highlight the potential of machine learning and neural networks, particularly in xVA calculations.…”

Section: Literature Reviewmentioning

confidence: 99%

Neural Networks for Credit Risk and xVA in a Front Office Pricing Environment

Frodé,

Sambergs,

Zhu

2022

SSRN Journal

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Section: Literature Reviewmentioning

confidence: 99%

Neural Networks for Credit Risk and xVA in a Front Office Pricing Environment

Frodé,

Sambergs,

Zhu

2022

SSRN Journal

Self Cite

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“…There are applications to assess the risk of individuals such as that of Kavitha [17] who proposes a model based on k-means, which has higher efficiency in accuracy and time compared to the traditional methods. For counterparty credit risk, Zhu, Chan, and Bright [18] apply machine learning techniques to determine and solve problems in XVA and credit profiles, through Monte Carlo simulation and K-means clustering method.…”

Section: Literature Reviewmentioning

confidence: 99%

Markov Chain K-Means Cluster Models and Their Use for Companies’ Credit Quality and Default Probability Estimation

2021

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This research aims to determine the existence of inflection points when companies’ credit risk goes from being minimal (Hedge) to being high (Ponzi). We propose an analysis methodology that determines the probability of hedge credits to migrate to speculative and then to Ponzi, through simulations with homogeneous Markov chains and the k-means clustering method to determine thresholds and migration among clusters. To prove this, we used quarterly financial data from a sample of 35 public enterprises over the period between 1 July 2006 and 28 March 2020 (companies listed on the USA, Mexico, Brazil, and Chile stock markets). For simplicity, we make the assumption of no revolving credits for the companies and that they face their next payment only with their operating cash flow. We found that Ponzi companies (1) have a 0.79 probability average of default, while speculative ones had (0) 0.28, and hedge companies (−1) 0.009, which are the inflections point we were looking for. Our work’s main limitation lies in not considering the entities’ behavior when granting credits in altered states (credit relaxation due to credit supply excess).

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Applying Machine Learning for Troubleshooting Credit Exposure and xVA Profiles

Cited by 2 publications

References 0 publications

Neural Networks for Credit Risk and xVA in a Front Office Pricing Environment

Neural Networks for Credit Risk and xVA in a Front Office Pricing Environment

Markov Chain K-Means Cluster Models and Their Use for Companies’ Credit Quality and Default Probability Estimation

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