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Creal, Drew
Schwaab, Bernd
Koopman, Siem Jan
Lucas, André
Year of Publication: 
Series/Report no.: 
ECB Working Paper No. 1626
European Central Bank (ECB), Frankfurt a. M.
We 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 modelling 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.
default risk
dynamic beta density
dynamic factor model
dynamic ordered probit
loss given default
panel data
Document Type: 
Working Paper

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