Explainable machine learning for financial risk management: two practical use cases

IF 1.2 4区数学 Q2 STATISTICS & PROBABILITY

Statistics Pub Date : 2024-09-13 DOI:10.1080/02331888.2024.2401078

Angelo Famà, Jurgena Myftiu, Paolo Pagnottoni, Alessandro Spelta

引用次数: 0

Abstract

We explore the potential of machine learning (ML) models applied in two financial risk management areas, i.e., credit risk management and financial risk hedging, through two practical use cases. Th...

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用于金融风险管理的可解释机器学习：两个实际应用案例

我们通过两个实际应用案例，探讨了机器学习（ML）模型在两个金融风险管理领域（即信用风险管理和金融风险对冲）中的应用潜力。这...

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来源期刊

Statistics 数学-统计学与概率论

CiteScore

1.00

自引率

0.00%

发文量

审稿时长

12 months

期刊介绍： Statistics publishes papers developing and analysing new methods for any active field of statistics, motivated by real-life problems. Papers submitted for consideration should provide interesting and novel contributions to statistical theory and its applications with rigorous mathematical results and proofs. Moreover, numerical simulations and application to real data sets can improve the quality of papers, and should be included where appropriate. Statistics does not publish papers which represent mere application of existing procedures to case studies, and papers are required to contain methodological or theoretical innovation. Topics of interest include, for example, nonparametric statistics, time series, analysis of topological or functional data. Furthermore the journal also welcomes submissions in the field of theoretical econometrics and its links to mathematical statistics.