Credit cycles and macro fundamentals

Koopman, Siem Jan; Kräussl, Roman; Lucas, André; Monteiro, André B.

doi:10.1016/j.jempfin.2008.07.002

Cited by 91 publications

(72 citation statements)

References 24 publications

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“…Previous research on credit risk has focused on a standard multinomial specification; see, for example, Koopman, Lucas, and Monteiro (2008) and Koopman, Kraeussl, Lucas, and Monteiro (2009). The multinomial density does not take into account the fact that ratings are ordered.…”

Section: Rating Transition Modelmentioning

confidence: 99%

Observation Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk

2011

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. All rights reserved. Terms of use: Documents in ISSN 1725-2806 (online) EU Catalogue NoQB-AR-13-123-EN-N (online)Any reproduction, publication and reprint in the form of a different publication, whether printed or produced electronically, in whole or in part, is permitted only with the explicit written authorisation of the ECB or the authors. AbstractWe propose a dynamic factor model for mixed-measurement and mixed-frequency panel data. In this framework time series observations may come from a range of families of parametric distributions, may be observed at different time frequencies, may have missing observations, and may exhibit common dynamics and cross-sectional dependence due to shared exposure to dynamic latent factors. The distinguishing feature of our model is that the likelihood function is known in closed form and need not be obtained by means of simulation, thus enabling straightforward parameter estimation by standard maximum likelihood. We use the new mixed-measurement framework for the signal extraction and forecasting of macro, credit, and loss given default risk conditions for U.S. Moody's-rated firms from January 1982 until March 2010. Our joint modeling framework allows us to construct predictive (conditional) loss densities for portfolios of corporate bonds in the presence of different sources of credit risk such as frailty effects and systematic recovery risk.Keywords: panel data; loss given default; default risk; dynamic beta density; dynamic ordered probit; dynamic factor model. JEL classification codes: C32, G32. Non-technical summaryCredit risk analysis has been highly relevant in the aftermath of the 2008 financial crisis.Financial institutions and supervisors are specifically trying to assess what is the common variation in firm defaults in order to better assess risk. In this paper we develop a novel dynamic factor modelling framework for panels of mixed measurement time series data. In this framework, observations may come from different families of distributions, may be observed at different frequencies such as monthly or quarterly, may have missing observations, and may exhibit cross-sectional dependence due to shared exposure to dynamic common latent factors.The main motivation is to obtain a flexible modeling framework for the estimation, analysis and forecasting of credit risk. A clear advantage of our framework is that the likelihood is available in closed form and need not be obtained by means of simulation. As a resu...

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Section: Rating Transition Modelmentioning

confidence: 99%

Observation Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk

2011

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“…Bonfim (2009) Koopman, Kräussl, Lucas & Monteiro (2009), os quais investigam os efeitos na inadimplência ou risco de crédito de fatores macroeconômicos de risco sistêmico e fatores de risco idiossincrático, que é o decorrente das características dos mutuários e das condições de empréstimos. O nosso objetivo com isso é dar suporte à especificação do nosso modelo econométrico.…”

Section: Inadimplência E Seus Determinantes: Modelos Macrofundamentadunclassified

Determinantes do risco de crédito rural no Brasil: uma crítica às renegociações da dívida rural

Melo

Filho

2017

Revista Brasileira De Economia

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“…This loss will be equal to the insured amount, which is defined as the minimum value among the invoice amount and the exposure the insurer has on the buyer. 2 Loss n = min (Invoice Amount n ; Exposure n )…”

Section: Loss Modelingmentioning

confidence: 99%

“…Many papers aim to find out whether business and credit cycles coincide, and it seems there is no final answer to this question (see, for example, [1,2] and the references therein). Both cycles seem to follow some macro-economic variables, and GDP variations seem to explain, at least partly, credit cycle movements.…”

Section: Introductionmentioning

confidence: 99%

Modeling Cycle Dependence in Credit Insurance

Caja

Planchet

2014

Risks

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Abstract:Business and credit cycles have an impact on credit insurance, as they do on other businesses. Nevertheless, in credit insurance, the impact of the systemic risk is even more important and can lead to major losses during a crisis. Because of this, the insurer surveils and manages policies almost continuously. The management actions it takes limit the consequences of a downturning cycle. However, the traditional modeling of economic capital does not take into account this important feature of credit insurance. This paper proposes a model aiming to estimate future losses of a credit insurance portfolio, while taking into account the insurer's management actions. The model considers the capacity of the credit insurer to take on less risk in the case of a cycle downturn, but also the inverse, in the case of a cycle upturn; so, losses are predicted with a more dynamic perspective. According to our results, the economic capital is over-estimated when not considering the management actions of the insurer.

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Credit cycles and macro fundamentals

Cited by 91 publications

References 24 publications

Observation Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk

Observation Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk

Determinantes do risco de crédito rural no Brasil: uma crítica às renegociações da dívida rural

Modeling Cycle Dependence in Credit Insurance

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