Yang, Bill Huajian and Du, Zunwei (2016): Rating Transition Probability Models and CCAR Stress Testing: Methodologies and implementations. Published in: Journal of Risk Model Validation , Vol. 10, No. 3 (September 2016): pp. 1-19.
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Abstract
Rating transition probability models, under the asymptotic single risk factor model framework, are widely used in the industry for stress testing and multi-period scenario loss projection. For a risk-rated portfolio, it is commonly believed that borrowers with higher risk ratings are more sensitive and vulnerable to adverse shocks. This means the asset correlation is required be differentiated between ratings and fully reflected in all respects of model fitting. In this paper, we introduce a risk component, called credit index, representing the part of systematic risk for the portfolio explained by a list of macroeconomic variables. We show that the transition probability, conditional to a list of macroeconomic variables, can be formulated analytically by using the credit index and the rating level sensitivity with respect to this credit index. Approaches for parameter estimation based on maximum likelihood for observing historical rating transition frequency, in presence of rating level asset correlation, are proposed. The proposed models and approaches are validated on a commercial portfolio, where we estimate the parameters for the conditional transition probability models, and project the loss for baseline, adverse and severely adverse supervisory scenarios provided by the Federal Reserve for the period 2016Q1-2018Q1. The paper explicitly demonstrates how Miu and Ozdemir’s original methodology ([5]) on transition probability models can be structured and implemented with rating specific asset correlation. It extends Yang and Du’s earlier work on this subject ([9]).We believe that the models and approaches proposed in this paper provide an effective tool to the practitioners for the use of transition probability models.
Item Type: | MPRA Paper |
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Original Title: | Rating Transition Probability Models and CCAR Stress Testing: Methodologies and implementations |
Language: | English |
Keywords: | CCAR stress testing, multi-period scenario, loss projection, credit index, risk sensitivity, asset correlation, transition frequency, transition probability, through-the-cycle, maximum likelihood |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C13 - Estimation: General 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 > C58 - Financial Econometrics 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 |
Item ID: | 76270 |
Depositing User: | Dr. Bill Huajian Yang |
Date Deposited: | 17 Jan 2017 10:36 |
Last Modified: | 27 Sep 2019 18:00 |
References: | [1] Ankarath, N., Ghost, T.P., Mehta, K.J., Alkafaji, Y. A. (2010), Understanding IFRS Fundamentals, John Wiley & Sons, Inc [1] Basel Committee on Banking Supervision (2015). The Interplay of Accounting and Regulation and its Impact on Banking Behaviour, January 2015. [2] Basel Committee on Banking Supervision (2015). Guidance on Accounting for Expected Credit Losses, February 2015. [2] Board of Governors of the Federal Reserve System (2016). Comprehensive Capital Analysis and Review 2016 Summary and Instructions, January 2016. [3] Gordy, M. B. (2003). A risk-factor model foundation for ratings-based bank capital rules. Journal of Financial Intermediation12, pp.199-232. doi:10.1016/S1042-9573(03)00040-8 [4] 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 [5] 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 [6] Vasicek, O. (2002). Loan portfolio value. RISK, December 2002, 160 - 162. [7] Wolfinger, R. (2008). Fitting Nonlinear Mixed Models with the New NLMIXED Procedure. SAS Institute Inc. [8] 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 doi:10.1016/j.jedc.2008.03.010 [9] Yang, B. H. , Zunwei Du (2015). Stress testing and modeling of rating migration under the Vasicek model framework, Journal of Risk Model Validation, Vol.9 (2), 2015 |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/76270 |