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 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...

Generalized Dynamic Factor Models for Mixed-Measurement Time Series

“…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).…”

“…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.…”

Cyclicality in losses on bank loans

“…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).…”

Global Credit Risk: World, Country and Industry Factors

“…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 ().…”

“…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.…”