4,508 research outputs found
Convex risk measures for good deal bounds
We study convex risk measures describing the upper and lower bounds of a good
deal bound, which is a subinterval of a no-arbitrage pricing bound. We call
such a convex risk measure a good deal valuation and give a set of equivalent
conditions for its existence in terms of market. A good deal valuation is
characterized by several equivalent properties and in particular, we see that a
convex risk measure is a good deal valuation only if it is given as a risk
indifference price. An application to shortfall risk measure is given. In
addition, we show that the no-free-lunch (NFL) condition is equivalent to the
existence of a relevant convex risk measure which is a good deal valuation. The
relevance turns out to be a condition for a good deal valuation to be
reasonable. Further we investigate conditions under which any good deal
valuation is relevant
Multivariate risks and depth-trimmed regions
We describe a general framework for measuring risks, where the risk measure
takes values in an abstract cone. It is shown that this approach naturally
includes the classical risk measures and set-valued risk measures and yields a
natural definition of vector-valued risk measures. Several main constructions
of risk measures are described in this abstract axiomatic framework.
It is shown that the concept of depth-trimmed (or central) regions from the
multivariate statistics is closely related to the definition of risk measures.
In particular, the halfspace trimming corresponds to the Value-at-Risk, while
the zonoid trimming yields the expected shortfall. In the abstract framework,
it is shown how to establish a both-ways correspondence between risk measures
and depth-trimmed regions. It is also demonstrated how the lattice structure of
the space of risk values influences this relationship.Comment: 26 pages. Substantially revised version with a number of new results
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Beyond cash-additive risk measures: when changing the num\'{e}raire fails
We discuss risk measures representing the minimum amount of capital a
financial institution needs to raise and invest in a pre-specified eligible
asset to ensure it is adequately capitalized. Most of the literature has
focused on cash-additive risk measures, for which the eligible asset is a
risk-free bond, on the grounds that the general case can be reduced to the
cash-additive case by a change of numeraire. However, discounting does not work
in all financially relevant situations, typically when the eligible asset is a
defaultable bond. In this paper we fill this gap allowing for general eligible
assets. We provide a variety of finiteness and continuity results for the
corresponding risk measures and apply them to risk measures based on
Value-at-Risk and Tail Value-at-Risk on spaces, as well as to shortfall
risk measures on Orlicz spaces. We pay special attention to the property of
cash subadditivity, which has been recently proposed as an alternative to cash
additivity to deal with defaultable bonds. For important examples, we provide
characterizations of cash subadditivity and show that, when the eligible asset
is a defaultable bond, cash subadditivity is the exception rather than the
rule. Finally, we consider the situation where the eligible asset is not
liquidly traded and the pricing rule is no longer linear. We establish when the
resulting risk measures are quasiconvex and show that cash subadditivity is
only compatible with continuous pricing rules
MULTIVARIATE RISKS AND DEPTH-TRIMMED REGIONS
We describe a general framework for measuring risks, where the risk measure takes values in an abstract cone. It is shown that this approach naturally includes the classical risk measures and set-valued risk measures and yields a natural definition of vector-valued risk measures. Several main constructions of risk measures are described in this axiomatic framework. It is shown that the concept of depth-trimmed (or central) regions from the multivariate statistics is closely related to the definition of risk measures. In particular, the halfspace trimming corresponds to the Value-at-Risk, while the zonoid trimming yields the expected shortfall. In the abstract framework, it is shown how to establish a both-ways correspondence between risk measures and depth-trimmed regions. It is also demonstrated how the lattice structure of the space of risk values influences this relationship.
Measuring risk with multiple eligible assets
The risk of financial positions is measured by the minimum amount of capital
to raise and invest in eligible portfolios of traded assets in order to meet a
prescribed acceptability constraint. We investigate nondegeneracy, finiteness
and continuity properties of these risk measures with respect to multiple
eligible assets. Our finiteness and continuity results highlight the interplay
between the acceptance set and the class of eligible portfolios. We present a
simple, alternative approach to the dual representation of convex risk measures
by directly applying to the acceptance set the external characterization of
closed, convex sets. We prove that risk measures are nondegenerate if and only
if the pricing functional admits a positive extension which is a supporting
functional for the underlying acceptance set, and provide a characterization of
when such extensions exist. Finally, we discuss applications to set-valued risk
measures, superhedging with shortfall risk, and optimal risk sharing
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