Journal of Financial Risk Management Vol.04 No.01(2015), Article
ID:54403,3 pages
10.4236/jfrm.2015.41003
Rearrangement Invariant, Coherent Risk Measures on L0
Christos E. Kountzakis, Dimitrios G. Konstantinides
Department of Mathematics, University of the Aegean, Karlovassi, Greece
Email: chrkoun@aegean.gr, konstant@aegean.gr
Copyright © 2015 by authors and Scientific Research Publishing Inc.
This work is licensed under the Creative Commons Attribution International License (CC BY).
http://creativecommons.org/licenses/by/4.0/
Received 5 January 2015; accepted 2 March 2015; published 5 March 2015
ABSTRACT
By this paper, we give an answer to the problem of definition of coherent risk measures
on rearrangement invariant, solid subspaces of L0 with respect to some
atom less probability space.
This problem was posed by F. Delbaen, while in this paper we proposed a solution
via ideals of L0 and the class of the dominated variation distributions,
as well.
Keywords:
Rearrangement Invariance, Dominated Variation, Moment-Index
1. Introduction
In (Delbaen, 2009) , the problem of defining a
risk measure on a solid, rearrangement invariant subspace of -space of random
variables with respect to some atomless probability space
. We recall that a vector space E, being a vector subspace of
is called rearrangement invariant if for random rariables
, which have
the same distribution,
implies
. Also, the
space E is solid if for andom viariables
,
, implies
. In (Delbaen, 2009) , there is an extensive treatment of
this problem, related to the role of the spaces
and
, compared
to E, especially in (Delbaen, 2009) . On the other
hand, the whole paper (Delbaen, 2002) is devoted
to the difficulties of defining coherent risk measures on subspaces of
, while it is proved that if the probability space is atomless,
no coherent risk measure is defined all over
(Delbaen, 2002) . Of course these attempts of
moving from
to appropriately defined subspaces of
, are related to the tail propertes of the random variables in
actuarial science and finance and more specifically to heavy-tailed distributed
random variables. The actual problem behind these seminal article by F. Delbaen
is since we cannot define a coherent risk measure on the entire
,
whether subspaces of
which are both alike
and preserve nice distributional properties (from the aspect of heavy-tails). Especially,
we treat the rearrangement invariance in the sense of remaining in the same class
of distributions and not by requiring distributional invariance. This is the topic
of our paper.
2. Ideals of L0 and Heavy-Tailed Distributions
It is well-known that since
is a Riesz space, being ordered by the pointwise-
-a.e.
partial ordering ³, it would be taken as a Riesz subspace of
. Hence, it may
be considered to be an order-complete Riesz space. Let us take an element y of
,
which corresponds to a heavy-tailed random variable. This indicates that either
for
for
,
for any real number,
where
or
.
Heavy-tailed random variables may not have even a finite moment
.
On the other hand, according to (Aliprantis & Border,
1999) , the principal ideal
generated by y in E, endowed by the norm
is an AM-space with order unit.
We also have to mention the following relevant.
Lemma 2.1 If
and y is a heavy-tailed random variable, then every
is a heavy-tailed random variable.
Proof. Since,
we get that for the sets
,
the inclusion
holds, which implies
for the corresponding cumulative distri- bution functions. Since for the integral
holds for any,
this implies
for any.
We recall the class
of dominated variation distributions:
This class is a sub-class of heavy -tailed distributions, see (Cai & Tang, 2004) .
Theorem 2.2 If,
where
denotes the class of dominated variation distributions respectively, then for every
,
.
Proof. According to what is proved in (Cai & Tang,
2004) , the class
is convolution-closed, namely if
,
then
.
First, we have to prove that if
,
then
,
for any
.
Since
,
there exists some
such that
.
But
.
This is easy to prove, since if
for any,
then in order to prove that
for any,
then we get that the above limsup is equal to
for any.
Hence,
.
Moreover, we have to prove that if
,
then
.
From the previous Lemma,
From the properties of the tail function of z we also have that since
for
,
then
Hence,
Since,
which is the desired conclusion.
Hence we obtain subspaces E of,
which are actually the ideals
which satisfy the rearrangement invariance property, while they contain non-integrable
distributions, in the sense that for any
there is a maximum p for which the moment
exists in
.
Let us discuss more this question. A notion which is very important is the one of the moment index. We recall that the moment index for
a non-negative random variable x is equal to
We also recall that if,
then
,
see in (Seneta, 1976) ,
(Tang & Tsitsiashvili, 2003) . The use of the moment index in the specific
case is that despite the validity of the (Delbaen, 2002)
, due to the fact that the elements of
distributions lie in the class
,
we assure that at least in the ideal
,
we assure a general level of non-integrability of
,
given by a finite
.
About the question whether the class
is the greatest in which the specific Theorem holds, we have to mention that if
we move up to the class of the subexponential distributions, it is not convolution-closed,
see for example in (Leslie, 1989) . As it is also
well- known from (Aliprantis & Border, 1999)
, the dual space
of
is an AL-space, since the ideal
is an AM-space with unit
,
as mentioned above. Hence, we keep the dual pair
for any of the y described above.
3. Expected-Shortfall on Ideals of L0
Taking any
whose
,
and defining the corresponding dual pair
we may define an Expected Shortfall-form risk measure on.
We have to notice that
satisfies both the order and the distributional rearrangement property, as a subspace
of
.
This is due to the properties of the class
of the dominated variation distributions. Hence we use
(Kaina & Rüschendorf, 2009) of the dual (robust) representation
of the usual Expected Shortfall in order to prove the following.
Theorem 3.1 The functional,
where
is an
-coherent
risk measure, where
is such that
.
Proof.
1)
for any,
due to the order completeness of the ideal
(y-Translation Invariance).
2)
for any
(Subadditivity).
3)
for any
and
(Positive Homogeneity).
4) If
then for any
we get
.
Hence by taking suprema all over
,
we ger
(
-Monotonicity).
Finally, if we suppose that the dual pair
is a symmentric Riesz pair, or else that
has order-
continuous norm (see also (Aliprantis & Border, 1999) ), then the values of R are finite since they represent the supremum value of a weak-star continuous linear functional on a weak-star compact set, which is the box of
functionals.
Otherwise, the infinity of the values of R may be excused by the presence of heavy-tailed
distributions.
References
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