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32
Modeling international financial returns with a multivariate regime switching copula. Preprint, available under http://wwwm4.ma.tum.de/Papers/index.html
, 2008
"... Reserve Bank of Boston, and the Federal Reserve Bank of New York, as well as the participants of the ..."
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Cited by 11 (1 self)
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Reserve Bank of Boston, and the Federal Reserve Bank of New York, as well as the participants of the
Copula Bayesian Networks
, 2010
"... We present the Copula Bayesian Network model for representing multivariate continuous distributions, while taking advantage of the relative ease of estimating univariate distributions. Using a novel copulabased reparameterization of a conditional density, joined with a graph that encodes independen ..."
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Cited by 11 (8 self)
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We present the Copula Bayesian Network model for representing multivariate continuous distributions, while taking advantage of the relative ease of estimating univariate distributions. Using a novel copulabased reparameterization of a conditional density, joined with a graph that encodes independencies, our model offers great flexibility in modeling highdimensional densities, while maintaining control over the form of the univariate marginals. We demonstrate the advantage of our framework for generalization over standard Bayesian networks as well as tree structured copula models for varied reallife domains that are of substantially higher dimension than those typically considered in the copula literature. 1
Asymmetric CAPM dependence for large dimensions: The canonical vine autoregressive copula model. ECORE Discussion Paper
, 2008
"... We propose a new dynamic model for volatility and dependence in high dimensions, that allows for departures from the normal distribution, both in the marginals and in the dependence. The dependence is modeled with a dynamic canonical vine copula, which can be decomposed into a cascade of bivariate c ..."
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Cited by 10 (0 self)
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We propose a new dynamic model for volatility and dependence in high dimensions, that allows for departures from the normal distribution, both in the marginals and in the dependence. The dependence is modeled with a dynamic canonical vine copula, which can be decomposed into a cascade of bivariate conditional copulas. Due to this decomposition, the model does not suffer from the curse of dimensionality. The canonical vine autoregressive (CAVA) captures asymmetries in the dependence structure. The model is applied to 95 S&P500 stocks. For the marginal distributions, we use nonGaussian GARCH models, that are designed to capture skewness and kurtosis. By conditioning on the market index and on sector indexes, the dependence structure is much simplified and the model can be considered as a nonlinear version of the CAPM or of a market model
Paircopula constructions of multivariate copulas
"... The famous Sklar’s theorem (see [54]) allows to build multivariate distributions using a copula and marginal distributions. For the basic theory on copulas see the first chapter ([14]) or the books on copulas by Joe ([32]) and Nelson ([51]). Much emphasis has been put on the bivariate case and in [3 ..."
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Cited by 7 (2 self)
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The famous Sklar’s theorem (see [54]) allows to build multivariate distributions using a copula and marginal distributions. For the basic theory on copulas see the first chapter ([14]) or the books on copulas by Joe ([32]) and Nelson ([51]). Much emphasis has been put on the bivariate case and in [32] and [51] many examples of bivariate
Modelling Longitudinal Data using a PairCopula Decomposition of Serial Dependence
, 2009
"... Copulas have proven to be very successful tools for the flexible modelling of crosssectional dependence. In this paper we express the dependence structure of continuous time series data using a sequence of bivariate copulas. This corresponds to a type of decomposition recently called a ‘vine ’ in t ..."
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Cited by 6 (3 self)
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Copulas have proven to be very successful tools for the flexible modelling of crosssectional dependence. In this paper we express the dependence structure of continuous time series data using a sequence of bivariate copulas. This corresponds to a type of decomposition recently called a ‘vine ’ in the graphical models literature, where each copula is entitled a ‘paircopula’. We propose a Bayesian approach for the estimation of this dependence structure for longitudinal data. Bayesian selection ideas are used to identify any independence paircopulas, with the end result being a parsimonious representation of a timeinhomogeneous Markov process of varying order. Estimates are Bayesian model averages over the distribution of the lag structure of the Markov process. Overall, the paircopula construction is very general and the Bayesian approach generalises many previous methods for the analysis of longitudinal data. Both the reliability of the proposed Bayesian methodology, and the advantages of the paircopula formulation, are demonstrated via simulation and two examples. The first is an agricultural science example, while the second is an econometric model for the forecasting of intraday electricity load. For both examples the Bayesian paircopula model is substantially more flexible than longitudinal models employed previously.
Relieving the elicitation burden of Bayesian Belief Networks
"... In this paper we present a new method (EBBN) that aims at reducing the need to elicit formidable amounts of probabilities for Bayesian belief networks, by reducing the number of probabilities that need to be specified in the quantification phase. This method enables the derivation of a variable’s co ..."
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Cited by 1 (1 self)
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In this paper we present a new method (EBBN) that aims at reducing the need to elicit formidable amounts of probabilities for Bayesian belief networks, by reducing the number of probabilities that need to be specified in the quantification phase. This method enables the derivation of a variable’s conditional probability table (CPT) in the general case that the states of the variable are ordered and the states of each of its parent nodes can be ordered with respect to the influence they exercise. EBBN requires only a limited amount of probability assessments from experts to determine a variable’s full CPT and uses piecewise linear interpolation. The number of probabilities to be assessed in this method is linear in the number of conditioning variables. EBBN’s performance was compared with the results achieved by applying both the normal copula vine approach from Hanea & Kurowicka (2007), and by using a simple uniform distribution. 1
New Prospects on Vines
"... In this paper, we present a new method to use vine copulas that aims to make optimal use of their diversity. First, we introduce a new algorithm that can produce n! ∏n−3 2 i=1 i! different vine copulas in dimension n. Then, to handle the ensuing computational burden of estimating all of them, we int ..."
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In this paper, we present a new method to use vine copulas that aims to make optimal use of their diversity. First, we introduce a new algorithm that can produce n! ∏n−3 2 i=1 i! different vine copulas in dimension n. Then, to handle the ensuing computational burden of estimating all of them, we introduce a computationally efficient selection algorithm. Without such algorithms to build and select vine copulas, statistically efficient use of them would be computationally intractable, especially in higher dimensions. Another advantage of our methodology is that it offers great flexibility to the practitioner, offering a choice of the paircopulas used, the selection testused and the vine copulas used. Key words: Vines, Multivariate copulas, Model Selection
Interactive Expert Assignment of MinimallyInformative Copulae
, 2002
"... It is increasingly recognized that good reliability practice requires giving attention to the specification of joint distributions for failure times. If the marginal distributions are known then the problem of selecting a marginal distribution is equivalent to that of selecting a copula. ..."
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It is increasingly recognized that good reliability practice requires giving attention to the specification of joint distributions for failure times. If the marginal distributions are known then the problem of selecting a marginal distribution is equivalent to that of selecting a copula.
Proceedings of the 2002 Winter Simulation Conference
"... A simulation model is successful if it leads to policy action, i.e., if it is implemented. Studies show that for a model to be implemented, it must have good correspondence with the mental model of the system held by the user of the model. The user must feel confident that the simulation model corre ..."
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A simulation model is successful if it leads to policy action, i.e., if it is implemented. Studies show that for a model to be implemented, it must have good correspondence with the mental model of the system held by the user of the model. The user must feel confident that the simulation model corresponds to this mental model. An understanding of how the model works is required. Simulation models for implementation must be developed step by step, starting with a simple model, the simulation prototype. After this has been explained to the user, a more detailed model can be developed on the basis of feedback from the user. Software for simulation prototyping is discussed, e.g., with regard to the ease with which models and output can be explained and the speed with which small models can be written.