scholarly journals A Convergence Theorem for Bivariate Exponential Dispersion Models

2019 ◽  
Vol 15 (1) ◽  
pp. 176-184
Author(s):  
Lila Ricci ◽  
Gabriela Boggio
Keyword(s):  
Convergence Theorem ◽  
Dispersion Models ◽  
Bivariate Exponential ◽  
Exponential Dispersion Models

Exponential dispersion models and credibility

1998 ◽  
Vol 1998 (1) ◽  
pp. 89-96 ◽  
Author(s):  
Zinoviy M. Landsman ◽  
Udi E. Makov
Keyword(s):  
Dispersion Models ◽  
Exponential Dispersion Models

scholarly journals On Some Layer-Based Risk Measures with Applications to Exponential Dispersion Models

2010 ◽  
Vol 2010 ◽  
pp. 1-19 ◽  
Author(s):  
Olga Furman ◽  
Edward Furman
Keyword(s):  
Closed Form ◽  
General Framework ◽  
Risk Measures ◽  
Dispersion Models ◽  
Exponential Dispersion Models

Layer-based counterparts of a number of well-known risk measures have been proposed and studied. Namely, some motivations and elementary properties have been discussed, and the analytic tractability has been demonstrated by developing closed-form expressions in the general framework of exponential dispersion models.

scholarly journals Tail Conditional Expectations for Exponential Dispersion Models

2005 ◽  
Vol 35 (1) ◽  
pp. 189-209 ◽  
Author(s):  
Zinoviy Landsman ◽  
Emiliano A. Valdez
Keyword(s):  
Conditional Expectation ◽  
Value At Risk ◽  
Risk Measure ◽  
Inverse Gaussian ◽  
Dispersion Models ◽  
Average Amount ◽  
Tail Conditional Expectation ◽  
Conditional Expectations ◽  
Exponential Dispersion Models ◽  
Fixed Period

There is a growing interest in the use of the tail conditional expectation as a measure of risk. For an institution faced with a random loss, the tail conditional expectation represents the conditional average amount of loss that can be incurred in a fixed period, given that the loss exceeds a specified value. This value is typically based on the quantile of the loss distribution, the so-called value-at-risk. The tail conditional expectation can therefore provide a measure of the amount of capital needed due to exposure to loss. This paper examines this risk measure for “exponential dispersion models”, a wide and popular class of distributions to actuaries which, on one hand, generalizes the Normal and shares some of its many important properties, but on the other hand, contains many distributions of nonnegative random variables like the Gamma and the Inverse Gaussian.

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