Creditor recovery: The macroeconomic dependence of industry equilibrium

Mora, Nada

doi:10.1016/j.jfs.2015.04.004

Cited by 31 publications

(15 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Bonfim (2009), using a large sample of Portuguese firms found that both the macroeconomic situation (systemic risk) and firms' financial conditions were important when estimating default probabilities over time. Mora (2015) confirmed the importance of macroeconomic effects on LGD of defaulted US debt securities, but concluded that these differ depending on the industry. For Germany it was found that banks' loss rate was driven by exposure to the global economic situation in addition to the national loss rate, industry, maturity structure and regional factors (Memmel et al, 2015).…”

Section: Introductionmentioning

confidence: 86%

Is it obligor or instrument that explains recovery rate: Evidence from US corporate bond

Yao

Crook

Andreeva

2017

Journal of Financial Stability

View full text Add to dashboard Cite

This study investigates the impacts of unobservable firm heterogeneity on modelling corporate bond recovery rates at the instrument level. Based on the recovery information over a long horizon from 1986 to 2012, we find that an obligor-varying linear factor model presents significant improvements in explaining the variations of recovery rates with a remarkably high intra-class correlation being observed. It emphasizes that the inclusion of an obligor-varying random effect term has effectively explained the unobservable firm level information shared by instruments of the same issuer and thus results in an improvement of predictive accuracy of recovery rates. The empirical results show that the latent economic cyclical effects have been well represented by firm level heterogeneity, and strong evidence is presented for the normal distributional assumption of the recovery rates. Finally we demonstrate the choice of recovery rate models may influence portfolio risk with the obligor-varying factor model generating a more right clustered loss distribution than other regression methods on the aggregated portfolio.

show abstract

Section: Introductionmentioning

confidence: 86%

Is it obligor or instrument that explains recovery rate: Evidence from US corporate bond

Yao

Crook

Andreeva

2017

Journal of Financial Stability

View full text Add to dashboard Cite

show abstract

“…A relação entre a probabilidade de inadimplência e a probabilidade de recuperação de crédito tem espaço no debate acadêmico. Conforme reconhecem Khieu et al (2012), apesar de alguns estudos sustentarem não haver correlação entre a probabilidade de inadimplência e a taxa de recuperação de crédito, há pesquisas que defendem a existência de uma relação inversa entre a probabilidade de inadimplência e a de recuperação de crédito com base nas condições setoriais, macroeconômicas e temporais (Altman et al, 2005;Mora, 2015). No mesmo sentido, Acharya et al (2007) reconhecem o fato de os determinantes de risco de inadimplência e de risco de recuperação de crédito serem correlacionados, ainda que não perfeitamente.…”

Section: Fatores Determinantes De Inadimplência E De Recuperação De Cunclassified

“…Além disso, de forma distinta da expectativa criada pelos trabalhos de Altman et al (2005) e de Mora (2015), não foi identificada uma relação inversa clara entre as variáveis explicativas de recuperação de crédito e de inadimplência, nem mesmo uma clara correlação negativa entre as variáveis, o que poderia potencializar perdas decorrentes de má estimação de risco de crédito em instituições financeiras.…”

Section: Considerações Finaisunclassified

Determinantes de Inadimplência e de Recuperação de Crédito em um Banco de Desenvolvimento

Amaral

Iquiapaza

2020

BASE

View full text Add to dashboard Cite

Considering the latest financial crises and because of the increasingly importance of credit risk management to economic actors and academics, this research aims to identify the conditioning factors of non-performing loans and credit recovery of a development bank portfolio. We analyzed data of 20.241 loans granted to micro and small enterprises between November 2009 and November 2014. To achieve the research goals, non-performing loan and loan recovery models were estimated by logistic regression. The results point out the significant effects on determining non-performing loans and/or credit recovery of (i) individual characteristics, such as firm’s age, (ii) loan characteristics, like the financial product and the installment payment period, and (iii) lending relationship, such as the number of years of relationship with the development bank. Contrary to the arguments of part of the literature, the results do not suggest any reverse relation between non-performing loans and credit recovery explanatory variables, which could enhance the loss from credit risk misspecification.

show abstract

“…In addition, most of the contemporary literature concerning RR determinants does not focus specifically on bank loans. Altman and Kalotay (2014), using the same set of determinants to study bank loans and corporate bonds, combine bank loans with corporate bonds, whereas Mora (2015) investigates corporate bonds only. Bank loans are fundamentally different to other securities; typically, they are senior to traded corporate debt.…”

Section: Introductionmentioning

confidence: 99%

The determinants of bank loan recovery rates in good times and bad - new evidence

Wang

Forbes

Fenech

et al. 2018

Preprint

View full text Add to dashboard Cite

We find that factors explaining bank loan recovery rates vary depending on the state of the economic cycle. Our modeling approach incorporates a two-state Markov switching mechanism as a proxy for the latent credit cycle, helping to explain differences in observed recovery rates over time. We are able to demonstrate how the probability of default and certain loan-specific and other variables hold different explanatory power with respect to recovery rates over 'good' and 'bad' times in the credit cycle. That is, the relationship between recovery rates and certain loan characteristics, firm characteristics and the probability of default differs depending on underlying credit market conditions. This holds important implications for modelling capital retention, particularly in terms of countercyclicality.

show abstract

Creditor recovery: The macroeconomic dependence of industry equilibrium

Cited by 31 publications

References 39 publications

Is it obligor or instrument that explains recovery rate: Evidence from US corporate bond

Is it obligor or instrument that explains recovery rate: Evidence from US corporate bond

Determinantes de Inadimplência e de Recuperação de Crédito em um Banco de Desenvolvimento

The determinants of bank loan recovery rates in good times and bad - new evidence

Contact Info

Product

Resources

About