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Der Vortrag „Modelling Dependence“ von Edu Pristine ist Bestandteil des Kurses „Archiv - Market Risks“. Der Vortrag ist dabei in folgende Kapitel unterteilt:
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... of stochastic dependence when random variables show multivariate normal distribution functions or when they are elliptically distributed but with some limitations. If variables are independent, they exhibit zero correlation. ...
... is a joint distribution function with continuous marginal function Fx(x)=u for the X variable and continuous marginal function Fy(y)=v for the Y variable. Copulas describes the dependence structure of the joint distribution ...
... copula is where X & Y are negatively correlated or countermonotonic as they move in opposite directions. The Gaussian copula depends only on the correlation coefficient ?. Where -1<= ? <=1. The ? symbol is univariate std. normal distribution function. The t-copula is the generalized form of the Gaussian copula ...
... used to model multivariate extremes, where the Gaussian copula is not appropriate. ...
... continuously decreasing and convex such that ?(1) = 0. Another group of copulas is known as extreme value (EV) copulas. An EV copula is appropriate when the joint multivariate function is a multivariate generalized extreme value (GEV) distribution. ...
... are asymptotically dependent as long as ? < 1. Similarly in the Gaussian copula, X & Y are asymptotically dependent as long as ? <1. When these conditions hold, extreme events will be independent as they occur further out in the tail. If the correlation coefficient is greater than -1 in the ...
... multivariate distribution function to a collection of univariate marginal distribution functions. B.That joins a distribution function to a collection of univariate marginal distribution functions C.That joins a ...
... of Archimedean copula is continuously decreasing and convex such that (1)=0. C.The extreme value copula is appropriate when the joint multivariate distribution of two variables is a multivariate ...
... is where the random variables X & Y are independent II.Minimum copula is where the X & Y are negatively dependent or monotonic III.Maximum copula is where ...
... The Correct answer is Zero correlation implies that the returns are independent, if the returns are distributed non -elliptically. Zero correlation doesn't imply that the returns ...