Topic
Conditional dependence
About: Conditional dependence is a research topic. Over the lifetime, 392 publications have been published within this topic receiving 16574 citations.
Papers published on a yearly basis
Papers
More filters
TL;DR: In this paper, the authors test for asymmetry in a model of the dependence between the Deutsche mark and the yen, in the sense that a different degree of correlation is exhibited during joint appreciations against the U.S. dollar versus during joint depreciations.
Abstract: We test for asymmetry in a model of the dependence between the Deutsche mark and the yen, in the sense that a different degree of correlation is exhibited during joint appreciations against the U.S. dollar versus during joint depreciations. We consider an extension of the theory of copulas to allow for conditioning variables, and employ it to construct flexible models of the conditional dependence structure of these exchange rates. We find evidence that the mark‐dollar and yen‐dollar exchange rates are more correlated when they are depreciating against the dollar than when they are appreciating.
1,666 citations
TL;DR: In this article, F.R. Engle's autoregressive conditional heteroskedastic model is extended to permit parametric specifications for conditional dependence beyond the mean and variance.
Abstract: R. F. Engle's autoregressive conditional heteroskedastic model is extended to permit parametric specifications for conditional dependence beyond the mean and variance. The suggestion is to model the conditional density with a small number of parameters, and then model these parameters as functions of the conditioning information. This method is applied to two data sets. The first application is to the monthly excess holding yield on U.S. Treasury securities, where the conditional density used is a Student's t distribution. The second application is to the U.S. Dollar/Swiss Franc exchange rate, using a new skewed Student t conditional distribution. Copyright 1994 by Economics Department of the University of Pennsylvania and the Osaka University Institute of Social and Economic Research Association.
1,571 citations
TL;DR: A new graphical model, called a vine, for dependent random variables, which generalize the Markov trees often used in modelling high-dimensional distributions and is weakened to allow for various forms of conditional dependence.
Abstract: A new graphical model, called a vine, for dependent random variables is introduced. Vines generalize the Markov trees often used in modelling high-dimensional distributions. They differ from Markov trees and Bayesian belief nets in that the concept of conditional independence is weakened to allow for various forms of conditional dependence.
1,247 citations
TL;DR: A general formula for the density of a vine dependent distribution is derived, which generalizes the well-known density formula for belief nets based on the decomposition of belief nets into cliques and allows a simple proof of the Information Decomposition Theorem for a regular vine.
Abstract: A vine is a new graphical model for dependent random variables Vines generalize the Markov trees often used in modeling multivariate distributions They differ from Markov trees and Bayesian belief nets in that the concept of conditional independence is weakened to allow for various forms of conditional dependence A general formula for the density of a vine dependent distribution is derived This generalizes the well-known density formula for belief nets based on the decomposition of belief nets into cliques Furthermore, the formula allows a simple proof of the Information Decomposition Theorem for a regular vine The problem of (conditional) sampling is discussed, and Gibbs sampling is proposed to carry out sampling from conditional vine dependent distributions The so-called ‘canonical vines’ built on highest degree trees offer the most efficient structure for Gibbs sampling
836 citations
TL;DR: In this paper, a nonparametric test for Granger non-causality was proposed to avoid the over-rejection observed in the frequently used test proposed by Hiemstra and Jones [1994].
794 citations