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LOWER TAIL INDEPENDENCE OF HITTING TIMES OF TWO-DIMENSIONAL DIFFUSIONS

Published online by Cambridge University Press:  18 September 2017

David Saunders ,
Lung Kwan Tsui  and
Satish Iyengar
David Saunders
Affiliation:
Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada E-mail: [email protected]
Lung Kwan Tsui
Affiliation:
Independent Model Review, HSBC, Toronto, Canada E-mail: [email protected]
Satish Iyengar
Affiliation:
Department of Statistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA E-mail: [email protected]
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Abstract

The coefficient of tail dependence is a quantity that measures how extreme events in one component of a bivariate distribution depend on extreme events in the other component. It is well known that the Gaussian copula has zero tail dependence, a shortcoming for its application in credit risk modeling and quantitative risk management in general. We show that this property is shared by the joint distributions of hitting times of bivariate (uniformly elliptic) diffusion processes.

Keywords

hitting timeslarge deviationsSchilder's theoremsmall time
Type
Research Article
Information
Probability in the Engineering and Informational Sciences , Volume 32 , Issue 4 , October 2018 , pp. 522 - 535
Copyright
Copyright © Cambridge University Press 2017 

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