On Missing Data Imputation for IRB Models (eBook, PDF)
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Technical Report from the year 2021 in the subject Business economics - Banking, Stock Exchanges, Insurance, Accounting, grade: 1, , language: English, abstract: Often, model development starts with missing data treatment. For regulatory internal rating based (IRB) models, missing data raise data quality concerns around system and process whereas the randomness nature of missing is sometimes overlooked, resulting inappropriate choice of imputation methods, more importantly, the chosen imputation method could lead to issues that violate modelling assumptions in the later process. With ML and AI...
Technical Report from the year 2021 in the subject Business economics - Banking, Stock Exchanges, Insurance, Accounting, grade: 1, , language: English, abstract: Often, model development starts with missing data treatment. For regulatory internal rating based (IRB) models, missing data raise data quality concerns around system and process whereas the randomness nature of missing is sometimes overlooked, resulting inappropriate choice of imputation methods, more importantly, the chosen imputation method could lead to issues that violate modelling assumptions in the later process. With ML and AI methods introduced to regulatory modelling, impact of missing data will be more thoroughly investigated and challenged. This paper starts with issues arose from imputation processes in practice, then briefly review common approaches for missing data treatment. A candidate Bayesian approach is then proposed as an alternative. In conclusion, imputed results using the proposed approach improve the explanatory power of historical observations while housing multiple convergence conditions such as the train-test accuracy, likelihood of value distribution, cross-validation and challenger model performance. At the dawn of ML and AI algorithms coming to the regulatory IRB models, these properties are highly desired in the area.
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