Yang, Bill Huajian
(2017):
*Forward Ordinal Probability Models for Point-in-Time Probability of Default Term Structure.*
Forthcoming in: Journal of Risk Model Validation
(September 2017)

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## Abstract

Common ordinal models, including the ordered logit model and the continuation ratio model, are structured by a common score (i.e., a linear combination of a list of given explanatory variables) plus rank specific intercepts. Sensitivity with respect to the common score is generally not differentiated between rank outcomes. In this paper, we propose an ordinal model based on forward ordinal probabilities for rank outcomes. The forward ordinal probabilities are structured by, in addition to the common score and intercepts, the rank and rating (for a risk-rated portfolio) specific sensitivity. This rank specific sensitivity allows a risk rating to respond to its migrations to default, downgrade, stay, and upgrade accordingly. An approach for parameter estimation is proposed based on maximum likelihood for observing rank outcome frequencies. Applications of the proposed model include modeling rating migration probability for point-in-time probability of default term structure for IFRS9 expected credit loss estimation and CCAR stress testing. Unlike the rating transition model based on Merton model, which allows only one sensitivity parameter for all rank outcomes for a rating, and uses only systematic risk drivers, the proposed forward ordinal model allows sensitivity to be differentiated between outcomes and include entity specific risk drivers (e.g., downgrade history or credit quality changes for an entity in last two quarters can be included). No estimation of the asset correlation is required. As an example, the proposed model, benchmarked with the rating transition model based on Merton model, is used to estimate the rating migration probability and probability of default term structure for a commercial portfolio, where for each rating the sensitivity is differentiated between migrations to default, downgrade, stay, and upgrade. Results show that the proposed model is more robust.

Item Type: | MPRA Paper |
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Original Title: | Forward Ordinal Probability Models for Point-in-Time Probability of Default Term Structure |

Language: | English |

Keywords: | PD term structure, forward ordinal probability, common score, rank specific sensitivity, rating migration probability |

Subjects: | C - Mathematical and Quantitative Methods > C0 - General C - Mathematical and Quantitative Methods > C0 - General > C01 - Econometrics C - Mathematical and Quantitative Methods > C0 - General > C02 - Mathematical Methods C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C18 - Methodological Issues: General C - Mathematical and Quantitative Methods > C4 - Econometric and Statistical Methods: Special Topics C - Mathematical and Quantitative Methods > C5 - Econometric Modeling 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 > C63 - Computational Techniques ; Simulation Modeling E - Macroeconomics and Monetary Economics > E6 - Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook > E61 - Policy Objectives ; Policy Designs and Consistency ; Policy Coordination G - Financial Economics > G3 - Corporate Finance and Governance 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 > G38 - Government Policy and Regulation O - Economic Development, Innovation, Technological Change, and Growth > O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights |

Item ID: | 79934 |

Depositing User: | Dr. Bill Huajian Yang |

Date Deposited: | 30 Jun 2017 06:17 |

Last Modified: | 29 Sep 2019 21:26 |

References: | [1] Ankarath, N., Ghost, T.P., Mehta, K.J., Alkafaji, Y. A. (2010), Understanding IFRS Fundamentals, John Wiley & Sons, Inc. [2] Board of Governors of the Federal Reserve System (2016). Comprehensive Capital Analysis and Review 2016 Summary and Instructions, January 2016. [3] Derbali, A., Hallara, S. (2013), Analysis of default probability: A comparative theoretical approach between the Credit Portfolio View model and the CreditRisk+ model, International Journal of Business Management & Research, Vol. 3 (1), 157-170 [4] Diaz, D., Gemmill, G. (2002), A systematic comparison of two approaches to measuring credit risk: CreditMetrics versus CreditRisk+, Trans 27th ICA [5] Gordy, M. B. (2003). A risk-factor model foundation for ratings-based bank capital rules. Journal of Financial Intermediation12, pp.199-232. [6] Merton, R. (1974). On the pricing of corporate debt: the risk structure of interest rates. Journal of Finance, Volume 29 (2), 449-470. DOI: 10.1111/j.1540-6261.1974.tb03058.x [7] Miu, P., Ozdemir, B. (2009). Stress testing probability of default and rating migration rate with respect to Basel II requirements, Journal of Risk Model Validation, Vol. 3 (4) Winter 2009 [8] Nystrom, K., Skoglund, J. (2006), A credit risk model for large dimensional portfolios with application to economic capital, Journal of banking & finance, Vol. 30, 2163-2197. DOI:10.1016/j.jbankfin.2005.05.024 [9] Rosen, D., Saunders, D. (2009). Analytical methods for hedging systematic credit risk with linear factor portfolios. Journal of Economic Dynamics & Control, 33 (2009), 37-52 [10] Vasicek, O. (2002). Loan portfolio value. RISK, December 2002, 160 - 162. [11] Wei, J. Z. (2003), A multi-factor, credit migration model for sovereign and corporate debts, Journal of international money and finance, Vol. 22, 709-735. DOI:10.1016/S0261-5606(03)00052-4 [12] Wolfinger, R. (2008). Fitting Nonlinear Mixed Models with the New NLMIXED Procedure. SAS Institute Inc. [13] Yang, B. H. , Zunwei Du (2016). Rating Transition Probability Models and CCAR Stress Testing, Journal of Risk Model Validation 10 (3), 2016, 1-19. DOI: 10.21314/JRMV.2016.155 |

URI: | https://mpra.ub.uni-muenchen.de/id/eprint/79934 |