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Multivariate Regular Variation in Probability Theory
This paper provides a brief overview of various definitions of multivariate regularly varying functions and some of their applications in probability theory and related fields.
A. L. Yakymiv
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Guodong Xing, Shanchao Yang
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Toward a Copula Theory for Multivariate Regular Variation
Multivariate regular variation describes the relative decay rates of joint tail probabilities of a random vector with respect to tail probabilities of a norm of this random vector, and it is often used in studying heavy-tail phenomena observed in data analysis in various fields, such as finance and insurance.
Haijun Li
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Estimation of the tail exponent of multivariate regular variation
It is well known that the extreme behavior of multivariate observations can be characterized by a spectral measure and a tail exponent. The estimation of this latter quantity is considered here, which is grounded on the fact that every convex combination of a random vector having a multivariate regularly varying tail also has a univariate regularly ...
Moosup Kim, Sangyeol Lee
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Under the framework of multivariate regular variation, we obtain the asymptotic ratio between the tail distortion risk measure for portfolio loss and the sum of value-at-risk for single loss by a d...
Guodong Xing, Xiaoli Gan
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Asymptotic Analysis of the Loss Given Default in the Presence of Multivariate Regular Variation
Consider a portfolio of n obligors subject to possible default. We propose a new structural model for the loss given default, which takes into account the severity of default. Then we study the tail behavior of the loss given default under the assumption that the losses of the n obligors jointly follow a multivariate regular variation structure.
Qihe Tang, Zhongyi Yuan
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Simulation of stock prices based on multivariate regular variation
Moosup Kim
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