Parsimonious Higher Order Markov Models for Rating Transitions

Baena-Mirabete, S; Puig, Pedro

doi:10.1111/rssa.12267

Cited by 13 publications

(4 citation statements)

References 42 publications

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“…This analysis complements the existing literature on the Markov property and the rating momentum effect (cf. Altman & Kao, 1992;Baena-Mirabete & Puig, 2018;dos Reis et al, 2020;Frydman & Schuerman, 2008;Lando & Skødeberg, 2002;Mizen & Tsoukas, 2012). We find that corporations whose ratings remained the same for a longer period of time possess lower default risk than corporations whose ratings were subject to a recent rating change.…”

Section: Introductionmentioning

confidence: 46%

Multiperiod default probability forecasting

Blümke¹

2021

Journal of Forecasting

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Accounting standards require that financial institutions must measure default risk with respect to the full maturity of a financial instrument. This requires forecasting of future default probabilities. The forecast of future default probabilities concerns two aspects: forecasting macroeconomic scenarios and future average (with respect to the macroeconomy) default probabilities. The present paper addresses the modeling of future average default probabilities. For corporations and depending on the initial rating grade, default probabilities change in different patterns over time. For initial investment‐grade ratings, the risk of a default increases, whereas for the bottom of the rating scale, default probabilities decrease. To model these different patterns, the paper proposes to extend the existing discrete‐time survival model and to incorporate an additional time‐ and covariate‐dependent shape parameter into the hazard function. Using data from Standard & Poor's, the paper shows that the shape parameter is able to reproduce the different patterns. The proposed model is benchmarked against a model that does not employ a shape parameter, and the results show that the additional shape parameter improves in‐sample and out‐of‐sample prediction.

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Section: Introductionmentioning

confidence: 46%

Multiperiod default probability forecasting

Blümke¹

2021

Journal of Forecasting

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“…Rating actions, including upgrades and downgrades, constitute rating transition matrices, to which the public always pays attention. In many existing models, rating transition matrices are typically assumed to be generated by a Markov chain ( Wozabal and Hochreiter, 2012 ), mixture of Markov chains ( Frydman and Schuermann, 2008 ), or higher order Markov chains ( Baena-Mirabete and Puig, 2018 ). However, the Markovian assumption is usually invalid if the long memory exists.…”

Section: Long Memory In Physical Worldmentioning

confidence: 99%

COVID-19 and credit risk: A long memory perspective

Yin¹,

Han²,

Wong³

2022

Insurance: Mathematics and Economics

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The COVID-19 pandemic shows significant impacts on credit risk, which is the key concern of corporate bond holders such as insurance companies. Credit risk, quantified by agency credit ratings and credit default swaps (CDS), usually exhibits long-range dependence (LRD) due to potential credit rating persistence. With rescaled range analysis and a novel affine forward intensity model embracing a flexible range of Hurst parameters, our studies on Moody's rating data and CDS prices reveal that default intensities have shifted from the long-range to the short-range dependence regime during the COVID-19 period, implying that the historical credit performance becomes much less relevant for credit prediction during the pandemic. This phenomenon contrasts sharply with previous financial-related crises. Specifically, both the 2008 subprime mortgage and the Eurozone crises did not experience such a great decline in the level of LRD in sovereign CDS. Our work also sheds light on the use of historical series in credit risk prediction for insurers' investment.

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“…Banks often have lending portfolios, typically non‐retail, for which the number of observed defaults is small. This is frequently so for the portfolio of large financial institutions (Kiefer, ), the sovereign portfolio (Fuertes and Kalotychou, ; Orth, ), municipalities (Baena‐Mirabete and Puig, ) or special segments such as housing associations (Kazianka, ). These portfolios, for which default events are rare, are commonly called low‐default portfolios.…”

Section: Introductionmentioning

confidence: 99%

Estimating the Probability of Default for No-Default and Low-Default Portfolios

Blümke¹

2019

Journal of the Royal Statistical Society Series C: Applied Statistics

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Summary The paper proposes a sequential Bayesian updating approach to estimate default probabilities on rating grade level for no‐ and low‐default portfolios. Bayesian sequential updating enables default probabilities to be obtained also for those rating grades for which no defaults have been observed. The advantage of this approach is that it preserves the rank order of rating grades in the case of no defaults. Rank preservation is not ensured when using an identical prior distribution across all rating grades. We discuss Bayesian sequential updating for the beta–binomial model and a model incorporating the asymptotic single‐risk factor model of the Basel Accord. Practical aspects such as incorporating information from external sources and the margin of conservatism are addressed.

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Parsimonious Higher Order Markov Models for Rating Transitions

Cited by 13 publications

References 42 publications

Multiperiod default probability forecasting

Multiperiod default probability forecasting

COVID-19 and credit risk: A long memory perspective

Estimating the Probability of Default for No-Default and Low-Default Portfolios

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