Time series patterns in credit ratings

Parnes, Dror

doi:10.1016/j.frl.2007.09.001

Cited by 11 publications

(8 citation statements)

References 14 publications

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“…Some studies conclude that credit rating transitions can be adequately modeled as a Markov chain for typical forecast horizons (one or two years), based on in-sample datasets [32,33]. Furthermore, heterogeneity in default probability, measured by a credit rating scoring for instance, is shown to be of critical importance in affecting the shape of the loss distribution [34,35].…”

Section: Time-homogeneitymentioning

confidence: 99%

“…Several studies specify non-homogeneous models that incorporate the notion of cyclicality and treat credit rating migrations as a non-memory-less process [35][36][37]. Nickell et al [38], for instance, investigate the dependence of ratings transition probabilities on industry, country and stage of the business cycle and argue that the business cycle dimension is critical in explaining variations in transition probabilities.…”

Section: Time-homogeneitymentioning

confidence: 99%

“…Similarly, Truck and Rachev [53] propose a class of "directed difference indices" to apply on transition matrices and these measures exhibit high correlation with differences in credit VaRs. To better understand the economic structure of time dependence, Parnes [35] examines shifts in key macroeconomic business conditions, incorporating a scaled GDP variable, contraction-expansion states and the CBOE volatility index (VIX). A number of studies test models of rating transitions in a calendar time framework over discrete time slots [36,54,55].…”

Section: Macroeconomic Factorsmentioning

confidence: 99%

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Bank Credit Risk Management and Rating Migration Analysis on the Business Cycle

Gavalas

Syriopoulos

2014

IJFS

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Abstract:Credit risk measurement remains a critical field of top priority in banking finance, directly implicated in the recent global financial crisis. This paper examines the dynamic linkages between credit risk migration due to rating shifts and prevailing macroeconomic conditions, reflected in alternative business cycle states. An innovative empirical methodology applies to bank internal rating data, under different economic scenarios and investigates the implications of credit risk quality shifts for risk rating transition matrices. The empirical findings are useful and critical for banks to align to Basel guidelines in relation to core capital requirements and risk-weighted assets in the underlying loan portfolio.

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Section: Time-homogeneitymentioning

confidence: 99%

Section: Time-homogeneitymentioning

confidence: 99%

Section: Macroeconomic Factorsmentioning

confidence: 99%

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Bank Credit Risk Management and Rating Migration Analysis on the Business Cycle

Gavalas

Syriopoulos

2014

IJFS

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“…Alternatively, a logit Z-score model could elaborate on the risk properties of different loan facility types (Liao 1994;Agarwal and Taffler 2007;Zhao 2008). At a corporate or sovereign level, a random-effects OPM could depict interactions between credit lines and credit grades, taking into account additional critical risk factors (such as geopolitical uncertainty, political risk or state of the economy) that potentially affect the overall credit rating assessments (Mählmann 2006;Parnes 2007;Alsakka and Gwilym 2010).…”

Section: Gavalas and T Syriopoulosmentioning

confidence: 99%

An integrated credit rating and loan quality model: application to bank shipping finance

Gavalas

Syriopoulos

2014

Maritime Policy & Management

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“…However, this approach is generally not robust with respect to misspecification of the independence structure, since Altman and Kao (1992) and Parnes (2007) have already observed that there is an autocorrelation structure in time series data of credit ratings. If one imposes an improper independence assumption on DOPM, then one may suffer from a loss of prediction power.…”

Section: Introductionmentioning

confidence: 99%

Predicting issuer credit ratings using generalized estimating equations

Hwang¹

2013

Quantitative Finance

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The dynamic ordered probit model (DOPM) with autocorrelation structure is proposed as a model for credit risk forecasting. It is more appropriate than the DOPM with independence structure, because correlations among repeated credit ratings have been observed by Altman and Kao [J. Financ. Anal., 1992, 48, 64-75] and Parnes [Financ. Res. Lett., 2007, 4, 217-226]. The unknown parameters in the proposed model are estimated by a generalized estimating equations (GEE) approach (Lipsitz et al. [Statist. Med., 1994, 13, 1149-1163). The GEE approach has been applied in many applications to analyse correlated repeated data due to its less-stringent distributional assumptions and robustness properties. Real data examples are used to illustrate the proposed model. The empirical results confirm that the proposed model compares favorably to the usual DOPM with independence structure, in the sense that the out-of-sample total error rate produced by the former is not only of smaller magnitude, but also of lower volatility. Thus the proposed model is a useful alternative for credit risk forecasting.

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Time series patterns in credit ratings

Cited by 11 publications

References 14 publications

Bank Credit Risk Management and Rating Migration Analysis on the Business Cycle

Bank Credit Risk Management and Rating Migration Analysis on the Business Cycle

An integrated credit rating and loan quality model: application to bank shipping finance

Predicting issuer credit ratings using generalized estimating equations

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