636,562 research outputs found
On dynamic spectral risk measures, a limit theorem and optimal portfolio allocation
In this paper we propose the notion of continuous-time dynamic spectral
risk-measure (DSR). Adopting a Poisson random measure setting, we define this
class of dynamic coherent risk-measures in terms of certain backward stochastic
differential equations. By establishing a functional limit theorem, we show
that DSRs may be considered to be (strongly) time-consistent continuous-time
extensions of iterated spectral risk-measures, which are obtained by iterating
a given spectral risk-measure (such as Expected Shortfall) along a given
time-grid. Specifically, we demonstrate that any DSR arises in the limit of a
sequence of such iterated spectral risk-measures driven by lattice-random
walks, under suitable scaling and vanishing time- and spatial-mesh sizes. To
illustrate its use in financial optimisation problems, we analyse a dynamic
portfolio optimisation problem under a DSR.Comment: To appear in Finance and Stochastic
Comparisons for backward stochastic differential equations on Markov chains and related no-arbitrage conditions
Most previous contributions to BSDEs, and the related theories of nonlinear
expectation and dynamic risk measures, have been in the framework of continuous
time diffusions or jump diffusions. Using solutions of BSDEs on spaces related
to finite state, continuous time Markov chains, we develop a theory of
nonlinear expectations in the spirit of [Dynamically consistent nonlinear
evaluations and expectations (2005) Shandong Univ.]. We prove basic properties
of these expectations and show their applications to dynamic risk measures on
such spaces. In particular, we prove comparison theorems for scalar and vector
valued solutions to BSDEs, and discuss arbitrage and risk measures in the
scalar case.Comment: Published in at http://dx.doi.org/10.1214/09-AAP619 the Annals of
Applied Probability (http://www.imstat.org/aap/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Merging of Opinions under Uncertainty
We consider long-run behavior of agents assessing risk in terms of dynamic convex risk measures or, equivalently, utility in terms of dynamic variational preferences in an uncertain setting. By virtue of a robust representation, we show that all uncertainty is revealed in the limit and agents behave as expected utility maximizer under the true underlying distribution regardless of their initial risk anticipation. In particular, risk assessments of distinct agents converge. This result is a generalization of the fundamental Blackwell-Dubins Theorem, cp. [Blackwell & Dubins, 62], to convex risk. We furthermore show the result to hold in a non-time-consistent environment.Dynamic Convex Risk Measures, Multiple Priors, Uncertainty, Robust Representation, Time-Consistency, Blackwell-Dubins
Merging of Opinions under Uncertainty
We consider long-run behavior of agents assessing risk in terms of dynamic convex risk measures or, equivalently, utility in terms of dynamic variational preferences in an uncertain setting. By virtue of a robust representation, we show that all uncertainty is revealed in the limit and agents behave as expected utility maximizer under the true underlying distribution regardless of their initial risk anticipation. In particular, risk assessments of distinct agents converge. This result is a generalization of the fundamental Blackwell-Dubins Theorem, cp. [Blackwell & Dubins, 62], to convex risk. We furthermore show the result to hold in a non -time-consistent environment.Dynamic Convex Risk Measures, Multiple Priors, Uncertainty, Robust Representation, Time-Consistency, Blackwell-Dubins.
Time consistency of dynamic risk measures and dynamic performance measures generated by distortion functions
The aim of this work is to study risk measures generated by distortion
functions in a dynamic discrete time setup, and to investigate the
corresponding dynamic coherent acceptability indices (DCAIs) generated by
families of such risk measures. First we show that conditional version of
Choquet integrals indeed are dynamic coherent risk measures (DCRMs), and also
introduce the class of dynamic weighted value at risk measures. We prove that
these two classes of risk measures coincides. In the spirit of robust
representations theorem for DCAIs, we establish some relevant properties of
families of DCRMs generated by distortion functions, and then define and study
the corresponding DCAIs. Second, we study the time consistency of DCRMs and
DCAIs generated by distortion functions. In particular, we prove that such
DCRMs are sub-martingale time consistent, but they are not super-martingale
time consistent. We also show that DCRMs generated by distortion functions are
not weakly acceptance time consistent. We also present several widely used
classes of distortion functions and derive some new representations of these
distortions.Comment: This manuscript is accompanied by a supplement that contains some
technical, but important, results and their proof
Time consistency of multi-period distortion measures
Dynamic risk measures play an important role for the acceptance or non-acceptance of risks in a bank portfolio. Dynamic consistency and weaker versions like conditional and sequential consistency guarantee that acceptability decisions remain consistent in time. An important set of static risk measures are so-called distortion measures. We extend these risk measures to a dynamic setting within the framework of the notions of consistency as above. As a prominent example, we present the Tail-Value-at-Risk (TVaR
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