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dc.contributor.authorBoreiko D
dc.contributor.authorKaniovski Y
dc.contributor.authorPflug G
dc.contributor.editor
dc.date.accessioned2017-11-07T15:21:20Z
dc.date.available2017-11-07T15:21:20Z
dc.date.issued2017
dc.identifier.issn0927-7099
dc.identifier.urihttp://dx.doi.org/10.1007/s10614-016-9576-1
dc.identifier.urihttp://link.springer.com/article/10.1007/s10614-016-9576-1
dc.identifier.urihttp://hdl.handle.net/10863/3575
dc.description.abstractTwo models of dependent credit rating migrations governed by industry-specific Markovian matrices, are considered. Caused by macroeconomic factors, positive and negative unobserved tendencies, encoded as values “1” or “0” of the corresponding variables, modify the transition probabilities and render the evolutions dependent. They are neither synchronized across industry sectors, nor over credit classes: an upswing in some of them can coexist with a decline of the rest. The models are tested on Standard and Poor’s data. MATLAB optimization software and maximum likelihood estimators are used. Obtained distributions of the hidden variables demonstrate that the considered industries migrate asynchronously trough credit classes. Since downgrading probabilities are less affected by the unobserved tendencies, estimated by Monte-Carlo simulations distributions of defaults, exhibit lighter, than for the known coupling models, tails for schemes with asynchronously moving industries. Moreover, the lightest tails were obtained in the case of industry-specific transition matrices.en_US
dc.language.isoenen_US
dc.rights
dc.titleNumerical modeling of dependent credit rating transitions with asynchronously moving industriesen_US
dc.typeArticleen_US
dc.date.updated2017-05-05T08:28:32Z
dc.publication.title
dc.language.isiEN-GB
dc.journal.titleComputational Economics
dc.description.fulltextreserveden_US


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