Beyond the Gaussian copula: stochastic and local correlation
نویسندگان
چکیده
منابع مشابه
Extending Gaussian copula with jumps to match correlation smile
We present an extension to the Gaussian copula model with jumps. We mix normal distributions which have negative means and small weights with the standard normal distribution in the Gaussian copula model to generate jumps in the default probability distribution for each underlying credit. The means and weights of the new normal distributions are used to control the size and intensity of the jum...
متن کاملCopula Mixed-Membership Stochastic Blockmodel with Subgroup Correlation
The Mixed-Membership Stochastic Blockmodel (MMSB) is a popular framework for modeling social network relationships which fully exploits each individual node participation (or membership) in a social structure. Despite its powerful representations, this model makes an assumption that the distributions of relational membership indicators between the two nodes are independent. Under many social ne...
متن کاملPerturbed Gaussian Copula
Gaussian copula is by far the most popular copula used in the financial industry in default dependency modeling. However, it has a major drawback — it does not exhibit tail dependence, a very important property for copula. The essence of tail dependence is the interdependence when extreme events occur, say, defaults of corporate bonds. In this paper we show that some tail dependence can be rest...
متن کاملVariational Gaussian Copula Inference
We utilize copulas to constitute a unified framework for constructing and optimizing variational proposals in hierarchical Bayesian models. For models with continuous and non-Gaussian hidden variables, we propose a semiparametric and automated variational Gaussian copula approach, in which the parametric Gaussian copula family is able to preserve multivariate posterior dependence, and the nonpa...
متن کاملCopula Gaussian Graphical Models *
We propose a comprehensive Bayesian approach for graphical model determination in observational studies that can accommodate binary, ordinal or continuous variables simultaneously. Our new models are called copula Gaussian graphical models and embed graphical model selection inside a semiparametric Gaussian copula. The domain of applicability of our methods is very broad and encompass many stud...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: The Journal of Credit Risk
سال: 2007
ISSN: 1744-6619
DOI: 10.21314/jcr.2007.059