Point-in-Time PD Term Structure Models with Loan Credit Quality as a Component

Yang, Bill Huajian (2017): Point-in-Time PD Term Structure Models with Loan Credit Quality as a Component.

Abstract

Most point-in-time PD term structure models used in industry for stress testing and IFRS9 expected loss estimation apply only to macroeconomic scenarios. Loan level credit quality is not a factor in these models. In practice, credit profile at assessment time plays an important role in the performance of the loan during its lifetime. A forward-looking point-in-time PD term structure model with loan credit quality as a component is widely expected. In this paper, we propose a forward-looking point-in-time PD term structure model based on forward survival probability, extending the model proposed in [8] by including a loan specific credit quality score as a component. The model can be derived under the Merton model framework. Under this model, the forward survival probability for a forward term is driven by a loan credit quality score in addition to macroeconomic factors. Empirical results show, the inclusion of the loan specific credit score can significantly improve the performance of the model. The proposed approaches provide a tool for modeling point-in-time PD term structure in cases where loan credit profile is essential. The model can be implemented easily by using, for example, the SAS procedure PROC NLMIXED.

• Item Type: MPRA Paper
• Language: English
• Keywords: PD term structure, loan credit quality score, macroeconomic scenario, forward survival probability, maximum likelihood
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C10 - General; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General; C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics > C40 - General; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C51 - Model Construction and Estimation; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C52 - Model Evaluation, Validation, and Selection; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C54 - Quantitative Policy Modeling; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C58 - Financial Econometrics; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C61 - Optimization Techniques ; Programming Models ; Dynamic Analysis; C - Mathematical and Quantitative Methods > C6 - Mathematical Methods ; Programming Models ; Mathematical and Simulation Modeling > C65 - Miscellaneous Mathematical Tools; E - Macroeconomics and Monetary Economics > E5 - Monetary Policy, Central Banking, and the Supply of Money and Credit > E52 - Monetary Policy; G - Financial Economics > G3 - Corporate Finance and Governance > G31 - Capital Budgeting ; Fixed Investment and Inventory Studies ; Capacity; G - Financial Economics > G3 - Corporate Finance and Governance > G32 - Financing Policy ; Financial Risk and Risk Management ; Capital and Ownership Structure ; Value of Firms ; Goodwill; G - Financial Economics > G3 - Corporate Finance and Governance > G33 - Bankruptcy ; Liquidation; G - Financial Economics > G3 - Corporate Finance and Governance > G38 - Government Policy and Regulation; M - Business Administration and Business Economics ; Marketing ; Accounting ; Personnel Economics > M4 - Accounting and Auditing

References

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[2] Basel Committee on Banking Supervision (2015). Guidance on Accounting for Expected Credit Losses, February 2015.

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[8] Yang, B. H., Point-in-time PD term structure models for multi-period scenario loss projections, Journal of Risk Model Validation, Vol 11 (1), Spring 2017. DOI:10.21314/JRMV.2017.164