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~isPartOf:"BIS working papers"
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~language:"eng"
~person:"Florax, Raymond J. G. M."
~person:"Gil-Alaña, Luis A."
~person:"Heckman, James J."
~person:"Koopman, Siem Jan"
~person:"Ravazzolo, Francesco"
~subject:"Kreditrisiko"
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ECONIS (ZBW)
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41
Time series models with a common stochastic variance for analysing economic time series
Koopman, Siem Jan
;
Bos, Charles S.
-
2002
The linear Gaussian state space model for which the common variance istreated as a stochastic time-varying variable is considered for themodelling of economic time series. The focus of this paper is on thesimultaneous estimation of parameters related to the stochasticprocesses of the mean part...
Persistent link: https://www.econbiz.de/10011327834
Saved in:
42
In-sample bounds for time-varying parameters of observation driven models
Blasques, Francisco
;
Koopman, Siem Jan
;
Łasak, Katarzyna
; …
-
2015
We study the performance of two analytical methods and one simulation method for computing in-sample confidence bounds for time-varying parameters. These in-sample bounds are designed to reflect parameter uncertainty in the associated filter. They are applicable to the complete class of...
Persistent link: https://www.econbiz.de/10010484891
Saved in:
43
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
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44
Low frequency and weighted likelihood solutions for mixed frequency dynamic factor models
Blasques, Francisco
;
Koopman, Siem Jan
;
Mallee, Max I. P.
-
2014
The multivariate analysis of a panel of economic and financial time series with mixed frequencies is a challenging problem. The standard solution is to analyze the mix of monthly and quarterly time series jointly by means of a multivariate dynamic model with a monthly time index: artificial...
Persistent link: https://www.econbiz.de/10010391543
Saved in:
45
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
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46
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
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47
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:
48
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
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49
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:
50
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
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