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~isPartOf:"CREATES research paper"
~isPartOf:"Discussion paper / Tinbergen Institute"
~isPartOf:"Discussion paper series / UCL Economics"
~isPartOf:"Estudos e documentos de trabalho"
~language:"eng"
~person:"Florax, Raymond J. G. M."
~person:"Gil-Alaña, Luis A."
~person:"Heckman, James J."
~person:"Koopman, Siem Jan"
~person:"Moraleda Novo, Juan Manuel"
~subject:"Kreditrisiko"
~subject:"Maximum-Likelihood-Schätzung"
~subject:"Meta-Analyse"
~subject:"Schätzung"
~subject:"USA"
~subject:"United States"
~subject:"Volatilität"
~type:"book"
~type_genre:"Collection of articles written by one author"
~type_genre:"Handbuch"
~type_genre:"Non-commercial literature"
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Florax, Raymond J. G. M.
Gil-Alaña, Luis A.
Heckman, James J.
Koopman, Siem Jan
Moraleda Novo, Juan Manuel
Lucas, André
39
McAleer, Michael
18
Groot, Henri L. F. de
15
Bos, Charles S.
14
Teulings, Coen N.
13
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12
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11
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9
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Santucci de Magistris, Paolo
7
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7
Wijnbergen, Sweder van
7
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6
Creal, Drew
6
Poot, Jacques
6
Pozzi, Lorenzo
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Gautier, Pieter
5
Nielsen, Morten Ørregaard
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Ommeren, Jos van
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Ooms, Marius
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Scharth, Marcel
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Sluis, Pieter J. van der
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Asai, Manabu
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Daníelsson, Jón
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4
Grassi, Stefano
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4
Hillebrand, Eric
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CREATES research paper
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ECONIS (ZBW)
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31
Spillover dynamics for systemic risk measurement using spatial financial time series models
Blasques, Francisco
;
Koopman, Siem Jan
;
Lucas, André
; …
-
2014
We introduce a new model for time-varying spatial dependence. The model extends the well-known static spatial lag model. All parameters can be estimated conveniently by maximum likelihood. We establish the theoretical properties of the model and show that the maximum likelihood estimator for the...
Persistent link: https://www.econbiz.de/10010391531
Saved in:
32
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:
33
A dynamic multivariate heavy-tailed model for time-varying volatilities and correlations
Creal, Drew
;
Koopman, Siem Jan
;
Lucas, André
-
2010
We propose a new class of observation-driven time-varying parameter models for dynamic volatilities and correlations to handle time series from heavy-tailed distributions. The model adopts generalized autoregressive score dynamics to obtain a time-varying covariance matrix of the multivariate...
Persistent link: https://www.econbiz.de/10011380135
Saved in:
34
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:
35
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:
36
Numerically accelerated importance sampling for nonlinear non-Gaussian state space models
Koopman, Siem Jan
;
Lucas, André
;
Scharth, Marcel
-
2012
We introduce a new efficient importance sampler for nonlinear non-Gaussian state space models. We propose a general and efficient likelihood evaluation method for this class of models via the combination of numerical and Monte Carlo integration methods. Our methodology explores the idea that...
Persistent link: https://www.econbiz.de/10011386179
Saved in:
37
Modeling dynamic volatilities and correlations under skewness and fat tails
Zhang, Xin
;
Creal, Drew
;
Koopman, Siem Jan
;
Lucas, André
-
2011
We propose a new model for dynamic volatilities and correlations of skewed and heavy-tailed data. Our model endows the Generalized Hyperbolic distribution with time-varying parameters driven by the score of the observation density function. The key novelty in our approach is the fact that the...
Persistent link: https://www.econbiz.de/10011386468
Saved in:
38
A family of humped volatility structures
Mercurio, Fabio
;
Moraleda Novo, Juan Manuel
-
1996
Persistent link: https://www.econbiz.de/10000948314
Saved in:
39
Time series models with a common stochastic variance for analysing economic time series
Koopman, Siem Jan
;
Bos, Charles S.
-
2002
Persistent link: https://www.econbiz.de/10001718624
Saved in:
40
The stochastic volatility on mean model : empirical evidence from international stock markets
Koopman, Siem Jan
;
Uspensky, Eugenie Hol
-
2000
Persistent link: https://www.econbiz.de/10001472890
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