6 papers
Non-Steepness and Maximum Likelihood Estimation Properties of the Truncated Multivariate Normal Distributions
Michael Levine, Donald Richards, Jianxi Su
This article considers exponential families of truncated multivariate normal distributions with one-sided truncation for some or all coordinates. We observe that if all components…
Independence Properties of the Truncated Multivariate Elliptical Distributions
Michael Levine, Donald Richards, Jianxi Su
Truncated multivariate distributions arise extensively in econometric modelling when non-negative random variables are intrinsic to the data-generation process. More broadly, trunc…
Multiple risk factor dependence structures: Copulas and related properties
Jianxi Su, Edward Furman
Copulas have become an important tool in the modern best practice Enterprise Risk Management, often supplanting other approaches to modelling stochastic dependence. However, choosi…
A form of multivariate Pareto distribution with applications to financial risk measurement
Jianxi Su, Edward Furman
A new multivariate distribution possessing arbitrarily parametrized and positively dependent univariate Pareto margins is introduced. Unlike the probability law of Asimit et al. (2…
Tail dependence of the Gaussian copula revisited
Edward Furman, Alexey Kuznetsov, Jianxi Su +1
Tail dependence refers to clustering of extreme events. In the context of financial risk management, the clustering of high-severity risks has a devastating effect on the well-bein…
Paths and indices of maximal tail dependence
Edward Furman, Jianxi Su, Ričardas Zitikis
We demonstrate both analytically and numerically that the existing methods for measuring tail dependence in copulas may sometimes underestimate the extent of extreme co-movements o…