3091. Asymmetric Tail Dependence for Climate Risk
Invited abstract in session MD-9: Green Investment on Capital Market, stream OR in Finance and Insurance .
Monday, 14:30-16:00Room: Clarendon SR 2.01
Authors (first author is the speaker)
| 1. | Davide Lauria
|
| Dipartimento di Scienze aziendali, economiche e metodi quantitativi, University of Bergamo |
Abstract
This study presents a new class of concordance and tail dependence measures based on surface integrals.
Measures of tail dependence between random variables aim to numerically quantify the degree of association between their extreme realizations. Existing tail dependence coefficients (TDCs) are based on an asymptotic analysis of relevant conditional probabilities, and do not provide a complete framework in which to compare extreme dependence between two random variables. In fact, for many important classes of bivariate distributions, these coefficients take on non-informative boundary values. We propose a new approach by first considering global measures based on the surface area of the conditional cumulative probability in copula space. The measures could be approached by cumulating probability on either the lower left or upper right domain of the copula space, and offer the novel perspective of being able to differentiate asymmetric dependence with respect to direction of conditioning.
Keywords
- Financial Modelling
- Finance and Banking
- Risk Analysis and Management
Status: accepted
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