Modern Actuarial Risk Theory - R. Kaas, Edukacja, Aktuariat
[ Pobierz całość w formacie PDF ]
Modern Actuarial
Risk Theory
by
Rob Kaas
University of Amsterdam, The Netherlands
Marc Goovaerts
Catholic University ofLeuven, Belgium and
University of Amsterdam, The Netherlands
Jan Dhaene
Catholic University of Leuven, Belgium and
University of Amsterdam, The Netherlands
and
Michel Denuit
Université Catholique de Louvain, Belgium
KLUWER ACADEMIC PUBLISHERS
NEW YORK, BOSTON, DORDRECHT, LONDON, MOSCOW
eBook ISBN:
0-306-47603-7
Print ISBN:
0-7923-7636-6
©2002 Kluwer Academic Publishers
New York, Boston, Dordrecht, London, Moscow
Print ©2001 Kluwer Academic Publishers
Dordrecht
All rights reserved
No part of this eBook may be reproduced or transmitted in any form or by any means, electronic,
mechanical, recording, or otherwise, without written consent from the Publisher
Created in the United States of America
Visit Kluwer Online at:
and Kluwer's eBookstore at:
Foreword
Risk Theory has been identified and recognized as an important part of actuarial
education; this is for example documented by the Syllabus of the
Society of
Actuaries
and by the recommendations of the
Groupe Consultatif.
Hence it is
desirable to have a diversity of textbooks in this area.
I welcome the arrival of this new text in risk theory, which is original in several
respects. In the language of figure skating or gymnastics, the text has two parts, the
compulsory part and the free-style part. The compulsory part includes Chapters
1–4, which are compatible with official material of the Society of Actuaries.
This feature makes the text also useful to students who prepare themselves for
the actuarial exams. Other chapters are more of a free-style nature, for example
Chapter 10 (
Ordering of Risks,
a speciality of the authors). And I would like to
mention Chapter 8 in particular: to my knowledge, this is the first text in risk theory
with an introduction to
Generalized Linear Models.
Special pedagogical efforts have been made throughout the book. The clear
language and the numerous exercises are an example for this. Thus the book can
be highly recommended as a textbook.
I congratulate the authors to their text, and I would like to thank them also in the
name of students and teachers that they undertook the effort to translate their text
into English. I am sure that the text will be successfully used in many classrooms.
H.U. Gerber
Lausanne, October 3, 2001
v
Preface
This book gives a comprehensive survey of non-life insurance mathematics. It was
originally written for use with the actuarial science programs at the Universities
of Amsterdam and Leuven, but its Dutch version has been used at several other
universities, as well as by the Dutch Actuarial Society. It provides a link to the
further theoretical study of actuarial science. The methods presented can not only
be used in non-life insurance, but are also effective in other branches of actuarial
science, as well as, of course, in actuarial practice.
Apart from the standard theory, this text contains methods that are directly rele-
vant for actuarial practice, for instance the rating of automobile insurance policies,
premium principles and IBNR models. Also, the important actuarial statistical
tool of the Generalized Linear Models is presented. These models provide extra
features beyond ordinary linear models and regression which are the statistical
tools of choice for econometricians. Furthermore, a short introduction is given to
credibility theory. Another topic which always has enjoyed the attention of risk
theoreticians is the study of ordering of risks.
The book reflects the state of the art in actuarial risk theory. Quite a lot of the
results presented were published in the actuarial literature only in the last decade
of the previous century.
Models and paradigms studied
An essential element of the models of life insurance is the time aspect. Between
paying premiums and collecting the resulting pension, some decennia generally
vii
viii
PREFACE
elapse. This time-element is less prominently present in non-life insurance math-
ematics. Here, however, the statistical models are generally more involved. The
topics in the first five chapters of this textbook are basic for non-life actuarial
science. The remaining chapters contain short introductions to some other topics
traditionally regarded as non-life actuarial science.
1.
The expected utility model
The very existence of insurers can be explained by way of the expected utility
model. In this model, an insured is a risk averse and rational decision maker, who
by virtue of Jensen’s inequality is ready to pay more than the expected value of
his claims just to be in a secure financial position. The mechanism through which
decisions are taken under uncertainty is not by direct comparison of the expected
payoffs of decisions, but rather of the expected utilities associated with these pay-
offs.
2.
The individual risk model
In the individual risk model, as well as in the collective risk model that follows
below, the total claims on a portfolio of insurance contracts is the random variable
of interest. We want to compute, for instance, the probability that a certain capital
will be sufficient to pay these claims, or the value-at-risk at level 95% associated
with the portfolio, being the 95% quantile of its cumulative distribution function
(cdf). The total claims is modelled as the sum of all claims on the policies, which
are assumed independent. Such claims cannot always be modelled as purely dis-
crete random variables, nor as purely continuous ones, and we provide a notation
that encompasses both these as special cases. The individual model, though the
most realistic possible, is not always very convenient, because the available data
is used integrally and not in any way condensed. We study other techniques than
convolution to obtain results in this model. Using transforms like the moment
generating function helps in some special cases. Also, we present approximations
based on fitting moments of the distribution. The Central Limit Theorem, which
involves fitting two moments, is not sufficiently accurate in the important right-
hand tail of the distribution. Hence, we also look at two more refined methods
using three moments: the translated gamma approximation and the normal power
approximation.
3.
Collective risk models
A model that is often used to approximate the individual model is the collective risk
model. In this model, an insurance portfolio is viewed as a process that produces
claims over time. The sizes of these claims are taken to be independent, identically
distributed random variables, independent also of the number of claims generated.
[ Pobierz całość w formacie PDF ]