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~isPartOf:"CREATES research paper"
~isPartOf:"Discussion paper / Tinbergen Institute"
~isPartOf:"Discussion paper series / UCL Economics"
~isPartOf:"HWWA discussion paper"
~language:"eng"
~person:"Schwaab, Bernd"
~subject:"Credit risk"
~subject:"Kalman filter"
~subject:"Kreditrisiko"
~subject:"Mathematische Optimierung"
~subject:"Maximum-Likelihood-Schätzung"
~subject:"Meta-Analyse"
~subject:"Schätzung"
~subject:"USA"
~subject:"United States"
~subject:"Volatilität"
~subject:"World"
~type:"book"
~type_genre:"Arbeitspapier"
~type_genre:"Collection of articles written by one author"
~type_genre:"Handbuch"
~type_genre:"Non-commercial literature"
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Schwaab, Bernd
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68
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46
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CREATES research paper
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1
Global credit risk :
world
, country and industry factors
Schwaab, Bernd
;
Koopman, Siem Jan
;
Lucas, André
-
2015
area sovereign debt crises. We find that macro and default-specific
world
factors are a primary source of default …
Persistent link: https://www.econbiz.de/10010484886
Saved in:
2
The information in systemic risk rankings
Nucera, Federico
;
Schwaab, Bernd
;
Koopman, Siem Jan
; …
-
2015
We propose to pool alternative systemic risk rankings for financial institutions using the method of principal components. The resulting overall ranking is less affected by estimation uncertainty and model risk. We apply our methodology to disentangle the common signal and the idiosyncratic...
Persistent link: https://www.econbiz.de/10010532581
Saved in:
3
Systemic risk diagnostics : coincident indicators and early warning signals
Schwaab, Bernd
;
Lucas, André
;
Koopman, Siem Jan
-
2010
Persistent link: https://www.econbiz.de/10008771823
Saved in:
4
Macro, industry and frailty effects in defaults : the 2008 credit crisis in perspective
Koopman, Siem Jan
;
Lucas, André
;
Schwaab, Bernd
-
2010
We determine the magnitude and nature of systematic default risk using 1971{2009) default data from Moody's. We disentangle systematic risk factors due to business cycle effects, common default dynamics (frailty), and industry-specific dynamics (including contagion). To quantify the contribution...
Persistent link: https://www.econbiz.de/10011379607
Saved in:
5
Systemic risk diagnostics
Schwaab, Bernd
;
Lucas, André
;
Koopman, Siem Jan
-
2010
, and the rest of the
world
. Controlling for global,region-specific, and industry effects, we construct coincident measures …
Persistent link: https://www.econbiz.de/10011382067
Saved in:
6
A dynamic yield curve model with stochastic volatility and non-Gaussian interactions : an empirical study of non-standard monetary policy in the euro area
Mesters, Geert
;
Schwaab, Bernd
;
Koopman, Siem Jan
-
2014
We develop an econometric methodology for the study of the yield curve and its interactions with measures of non-standard monetary policy during possibly turbulent times. The yield curve is modeled by the dynamic Nelson-Siegel model while the monetary policy measurements are modeled as...
Persistent link: https://www.econbiz.de/10010362975
Saved in:
7
Observation driven mixed-measurement dynamic factor models with an application to credit risk
Creal, Drew
;
Schwaab, Bernd
;
Koopman, Siem Jan
;
Lucas, …
-
2011
This paper has been accepted for publication in the 'Review of Economics and Statistics'.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...
Persistent link: https://www.econbiz.de/10011383248
Saved in:
8
Measuring credit risk in a large banking system : econometric modeling and empirics
Lucas, André
;
Schwaab, Bernd
;
Zhang, Xin
-
2013
Persistent link: https://www.econbiz.de/10010191403
Saved in:
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