2011
DOI: 10.2139/ssrn.1765764
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Observation Driven Mixed-Measurement Dynamic Factor Models with an Application to Credit Risk

Abstract: 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… Show more

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Cited by 46 publications
(50 citation statements)
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References 31 publications
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“…In addition, we are not sure if unemployment rate is strongly correlated with default counts or bond recovery rates, although there is some weak evidence showing that default counts may be negatively associated and bond recovery rates may be positively associated with unemployment rates. This further demonstrates that systematic default and recovery risk does not coincide with business cycle risk, which is consistent with the related findings in Das et al (2007), Bruche and Gonzlez-Aguado (2010) and Creal et al (2011).…”
Section: Intertwined Corporate Default Recovery Rate and Business supporting
confidence: 90%
See 1 more Smart Citation
“…In addition, we are not sure if unemployment rate is strongly correlated with default counts or bond recovery rates, although there is some weak evidence showing that default counts may be negatively associated and bond recovery rates may be positively associated with unemployment rates. This further demonstrates that systematic default and recovery risk does not coincide with business cycle risk, which is consistent with the related findings in Das et al (2007), Bruche and Gonzlez-Aguado (2010) and Creal et al (2011).…”
Section: Intertwined Corporate Default Recovery Rate and Business supporting
confidence: 90%
“…Since recovery rates play a critical role in pricing and risk models, treating recovery rates as either constant or a stochastic variable independent of default rates while neglecting inverse relationship leads to inaccurate estimation of the loss function and suboptimal capital allocation. Furthermore, according to the current Basel proposal, banks can opt to provide their own recovery rate forecasts for the calculation of regulatory capital (Creal et al (2011)). Thus there is an immediate need for statistical models explaining the relationship between the corporate default risk and bond recovery rates (and probably some other credit risk indicators), which can be used in default prediction and credit risk modeling.…”
Section: Intertwined Corporate Default Recovery Rate and Business mentioning
confidence: 99%
“…We also extend Calabrese (2014a), who only models the LGD, by linking the LGD to the default rate and macro variables. We deviate from Creal et al (2014) and Bruche and González-Aguado (2010), who use the Beta distribution for the LGD, because the mixture of normals in our model can more easily accommodate observations outside [0, 1]. The default-specific factor also extends the single Markov-switching business cycle of Bruche and González-Aguado (2010).…”
Section: Introductionmentioning
confidence: 99%
“…This point is most compellingly made by Das et al (), who apply a multitude of statistical tests, and almost always reject the joint hypothesis that their default intensities are well specified in terms of (i) easily observed firm‐specific and macro‐financial information and (ii) the doubly stochastic default times assumption, also known as the conditional independence assumption. In particular, there is substantial evidence for an additional dynamic unobserved ‘frailty’ risk factor and/or contagion dynamics; see Koopman et al (), Duffie et al () and Creal et al ().…”
Section: Introductionmentioning
confidence: 99%
“…Moving to a monthly grid would increase the number of zero values and missing values, implying that the count data become even more sparse. Moving to a yearly frequency would substantially shorten the sample, implying that risk factor dynamics would not be estimated precisely Creal et al (2014). model default and rating transition data on a monthly grid; parameter and risk factor inference does not seem to be overly sensitive to the chosen frequency.…”
mentioning
confidence: 99%