Post by Sapphire Capital on Jul 11, 2008 7:35:01 GMT 4
Forecasting Cross-Sections of Frailty-Correlated Default
S.J. KOOPMAN
VU University Amsterdam; Tinbergen Institute
ANDRE LUCAS
Free University Amsterdam - Faculty of Economics and Econometrics; Tinbergen Institute - Tinbergen Institute Amsterdam (TIA)
BERND SCHWAAB
VU University; Tinbergen Institute - Tinbergen Institute Amsterdam (TIA)
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February 20, 2008
Tinbergen Institute Discussion Paper No. 08-029/4
Abstract:
We propose a novel econometric model for estimating and forecasting cross-sections of time-varying conditional default probabilities. The model captures the systematic variation in corporate default counts across e.g. rating and industry groups by using dynamic factors from a large panel of selected macroeconomic and financial data as well as common unobserved risk factors. All factors are statistically and economically significant and together capture a large part of the time-variation in observed default rates. In this framework we improve the out-of-sample forecasting accuracy associated with conditional default probabilities by about 10-35% in terms of Mean Absolute Error, particularly in years of default stress.
papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1113047_code356671.pdf?abstractid=1113047&mirid=2
S.J. KOOPMAN
VU University Amsterdam; Tinbergen Institute
ANDRE LUCAS
Free University Amsterdam - Faculty of Economics and Econometrics; Tinbergen Institute - Tinbergen Institute Amsterdam (TIA)
BERND SCHWAAB
VU University; Tinbergen Institute - Tinbergen Institute Amsterdam (TIA)
--------------------------------------------------------------------------------
February 20, 2008
Tinbergen Institute Discussion Paper No. 08-029/4
Abstract:
We propose a novel econometric model for estimating and forecasting cross-sections of time-varying conditional default probabilities. The model captures the systematic variation in corporate default counts across e.g. rating and industry groups by using dynamic factors from a large panel of selected macroeconomic and financial data as well as common unobserved risk factors. All factors are statistically and economically significant and together capture a large part of the time-variation in observed default rates. In this framework we improve the out-of-sample forecasting accuracy associated with conditional default probabilities by about 10-35% in terms of Mean Absolute Error, particularly in years of default stress.
papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID1113047_code356671.pdf?abstractid=1113047&mirid=2