Multiperiod conditional distribution functions for conditionally normal GARCH(1, 1) models

Multiperiod Conditional Distribution Functions for Conditionally Normal Garch(1, 1) Models

We study the asymptotic tail behavior of the conditional probability distributions of rt+k and rt+1 + · · · + rt+k when (rt )t∈N is a GARCH(1, 1) process. As an application, we examine the relation between the extreme lower quantiles of these random variables.

Lattice Models for Conditional Independence in a Multivariate Normal Distribution

On Conditional Applications of Matrix Variate Normal Distribution

In this paper, by conditioning on the matrix variate normal distribution (MVND) the construction of the matrix t-type family is considered, thus providing a new perspective of this family. Some important statistical characteristics are given. The presented t-type family is an extension to the work of Dickey [8]. A Bayes estimator for the column covariance matrix &Sigma of MVND is derived under ...

Conditional Point Distribution Models

In this paper, we propose an efficient method for drawing shape samples using a point distribution model (PDM) that is conditioned on given points. This technique is suited for sample-based segmentation methods that rely on a PDM, e.g. [6], [2] and [3]. It enables these algorithms to effectively constrain the solution space by considering a small number of user inputs – often one or two landmar...

Approximating Conditional Distribution Functions Using Dimension Reduction

Motivated by applications to prediction and forecasting, we suggest methods for approximating the conditional distribution function of a random variable Y given a dependent random d-vector X. The idea is to estimate not the distribution of Y |X, but that of Y |θX, where the unit vector θ is selected so that the approximation is optimal under a least-squares criterion. We show that θ may be esti...