Understanding the Recovery Rates on Defaulted Securities

Acharya, Viral V.; Bharath, Sreedhar T.; Srinivasan, Anand

doi:10.2139/ssrn.442901

Cited by 94 publications

(112 citation statements)

References 37 publications

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“…The coupon rate has a positive and marginally significant coefficient in some models, which is to be expected given the results in Acharya et al (2003). The impact of the logarithm of maturity outstanding is negative and also marginally significant in some models, while the logarithm of the issue size is not significant, similar to results from Acharya et al (2003).…”

Section: Recovery Rate Modelssupporting

confidence: 71%

“…For comparison, we fitted the four default models first with a shared frailty and then with a multiplicative frailty in the hazard rate. 1 In the multiplicative frailty models the frailty variance θ is statistically significant, providing evidence of default clustering. The frailty multiplicative factor ∆ is also highly statistically significant, and its magnitude …”

Section: Default Models: In-sample Estimatesmentioning

confidence: 99%

“…In this paper we denote the specification given by (1) as the multiplicative frailty model, since the ∆ factor acts multiplicatively on the hazard rate. A special case of the multiplicative frailty model is obtained when ∆ = 1.…”

Section: The Default Modelsmentioning

confidence: 99%

“…Our goal is not to derive new predictive models for recovery rates or to investigate exhaustively the determinants of recoveries, since extant literature has already focused on this topic (Acharya et al, 2003 and. The modelling of recovery rates in this paper is only a preliminary stage towards the ultimate objective of assessing the impact of default and recovery models on predicted loss.…”

Section: Recovery Rate Modelsmentioning

confidence: 99%

“…There is a large and growing literature devoted to the modeling of the probability of default -see Shumway There is also an emerging literature addressing the modeling of the determinants of the recovery rate given default. A survey of empirical evidence regarding the properties of recovery rates is given in Schuermann (2004) -see also Acharya, Bharath and Srinivasan (2003). Several studies model the dependence between the probability of default and the recovery rate given default, by assuming there is a common latent factor affecting both (Frye, 2000;Dullmann and Trapp, 2004).…”

Section: Introductionmentioning

confidence: 99%

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Modeling the Loss Distribution

2011

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In this paper we focus on modeling and predicting the loss distribution for credit risky assets such as bonds and loans. We model the probability of default and the recovery rate given default based on shared covariates. We develop a new class of default models that explicitly account for sector specific and regime dependent unobservable heterogeneity in firm characteristics. Based on the analysis of a large default and recovery data set over the horizon 1980-2008, we document that the specification of the default model has a major impact on the predicted loss distribution, while the specification of the recovery model is less important. In particular, we find evidence that industry factors and regime dynamics affect the performance of default models, implying that the appropriate choice of default models for loss prediction will depend on the credit cycle and on portfolio characteristics. Finally, we show that default probabilities and recovery rates predicted out-of-sample are negatively correlated, and that the magnitude of the correlation varies with seniority class, industry, and credit cycle.

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Section: Recovery Rate Modelssupporting

confidence: 71%

Section: Default Models: In-sample Estimatesmentioning

confidence: 99%

Section: The Default Modelsmentioning

confidence: 99%

Section: Recovery Rate Modelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Modeling the Loss Distribution

2011

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Default Recovery Rates and LGD in Credit Risk Modeling and Practice

2008

Managing Credit Risk

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Evidence from many countries in recent years suggests that collateral values and recovery rates on corporate defaults can be volatile and, moreover, that they tend to go down just when the number of defaults goes up in economic downturns. This link between recovery rates and default rates has traditionally been neglected by credit risk models, as most of them focused on default risk and adopted static loss assumptions, treating the recovery rate either as a constant parameter or as a stochastic variable independent from the probability of default. This traditional focus on default analysis has been partly reversed by the recent significant increase in the number of studies dedicated to the subject of recovery rate estimation and the relationship between default and recovery rates. This paper presents a detailed review of the way credit risk models, developed during the last thirty years, treat the recovery rate and, more specifically, its relationship with the probability of default of an obligor. We also review the efforts by rating agencies to formally incorporate recovery ratings into their assessment of corporate loan and bond credit risk and the recent efforts by the Basel Committee on Banking Supervision to consider "downturn LGD" in their suggested requirements under Basel II. Recent empirical evidence concerning these issues and the latest data on high-yield bond and leverage loan defaults is also presented and discussed.

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References

2009

Active Credit Portfolio Management in Practice

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Understanding the Recovery Rates on Defaulted Securities

Cited by 94 publications

References 37 publications

Modeling the Loss Distribution

Modeling the Loss Distribution

Default Recovery Rates and LGD in Credit Risk Modeling and Practice

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