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Law-invariant functionals beyond bounded positions
02 Aug 2018-
TL;DR: In this paper, the equivalence of law invariance and Schur convexity for law-invariant functions is established for a large class of spaces of random variables.
Abstract: We establish general versions of a variety of results for quasiconvex, lower-semicontinuous, and law-invariant functionals. Our results extend well-known results from the literature to a large class of spaces of random variables. We sometimes obtain sharper versions, even for the well-studied case of bounded random variables. Our approach builds on two fundamental structural results for law-invariant functionals: the equivalence of law invariance and Schur convexity, i.e., monotonicity with respect to the convex stochastic order, and the fact that a law-invariant functional is fully determined by its behaviour on bounded random variables. We show how to apply these results to provide a unifying perspective on the literature on law-invariant functionals, with special emphasis on quantile-based representations, including Kusuoka representations, dilatation monotonicity, and infimal convolutions.
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01 Jan 2016
TL;DR: The non additive measure and integral is universally compatible with any devices to read and is available in the book collection an online access to it is set as public so you can get it instantly.
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136 citations
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TL;DR: In this paper, it was shown that the expectation functional is the only law-invariant convex functional that is linear along the direction of a nonconstant random variable with nonzero expectation.
Abstract: We discuss when law-invariant convex functionals "collapse to the mean". More precisely, we show that, in a large class of spaces of random variables and under mild semicontinuity assumptions, the expectation functional is, up to an affine transformation, the only law-invariant convex functional that is linear along the direction of a nonconstant random variable with nonzero expectation. This extends results obtained in the literature in a bounded setting and under additional assumptions on the functionals. We illustrate the implications of our general results for pricing rules and risk measures.
4 citations
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01 Jan 1976TL;DR: In this article, the authors consider non-convex variational problems with a priori estimate in convex programming and show that they can be solved by the minimax theorem.
Abstract: Preface to the classics edition Preface Part I. Fundamentals of Convex Analysis. I. Convex functions 2. Minimization of convex functions and variational inequalities 3. Duality in convex optimization Part II. Duality and Convex Variational Problems. 4. Applications of duality to the calculus of variations (I) 5. Applications of duality to the calculus of variations (II) 6. Duality by the minimax theorem 7. Other applications of duality Part III. Relaxation and Non-Convex Variational Problems. 8. Existence of solutions for variational problems 9. Relaxation of non-convex variational problems (I) 10. Relaxation of non-convex variational problems (II) Appendix I. An a priori estimate in non-convex programming Appendix II. Non-convex optimization problems depending on a parameter Comments Bibliography Index.
4,434 citations
Book•
01 Jan 1987
TL;DR: In this article, the classical interpolation theorem is extended to the Banach Function Spaces, and the K-Method is used to find a Banach function space with a constant number of operators.
Abstract: Banach Function Spaces. Rearrangement-Invariant Banach Function Spaces. Interpolation of Operators on Rearrangement-Invariant Spaces. The Classical Interpolation Theorems. The K-Method. Each chapter includes references. Index.
3,388 citations
Book•
01 Jan 2002
TL;DR: In this article, the authors present an introduction to financial mathematics, focusing on stochastic models in discrete time, with a focus on the problem of pricing and hedging of financial derivatives.
Abstract: This book is an introduction to financial mathematics. It is intended for graduate students in mathematics and for researchers working in academia and industry. The focus on stochastic models in discrete time has two immediate benefits. First, the probabilistic machinery is simpler, and one can discuss right away some of the key problems in the theory of pricing and hedging of financial derivatives. Second, the paradigm of a complete financial market, where all derivatives admit a perfect hedge, becomes the exception rather than the rule. Thus, the need to confront the intrinsic risks arising from market incomleteness appears at a very early stage. The first part of the book contains a study of a simple one-period model, which also serves as a building block for later developments. Topics include the characterization of arbitrage-free markets, preferences on asset profiles, an introduction to equilibrium analysis, and monetary measures of financial risk. In the second part, the idea of dynamic hedging of contingent claims is developed in a multiperiod framework. Topics include martingale measures, pricing formulas for derivatives, American options, superhedging, and hedging strategies with minimal shortfall risk. This third revised and extended edition now contains more than one hundred exercises. It also includes new material on risk measures and the related issue of model uncertainty, in particular a new chapter on dynamic risk measures and new sections on robust utility maximization and on efficient hedging with convex risk measures.
1,866 citations
Book•
01 Jan 2002
TL;DR: In this article, the authors present preliminary results on functional analysis and convex analysis in Locally Convex Spaces (LCS) and describe some applications of convex analyses in Normed Spaces.
Abstract: Preliminary Results on Functional Analysis Convex Analysis in Locally Convex Spaces Some Results and Applications of Convex Analysis in Normed Spaces.
1,733 citations
Book•
01 Jan 1994
TL;DR: In this article, the authors propose the integration of Monotone Functions on Intervals and the construction of measures using topology, based on the Radon-Nikodym Theorem.
Abstract: Preface. 1. Integration of Monotone Functions on Intervals. 2. Set Functions and Caratheodory Measurability. 3. Construction of Measures using Topology. 4. Distribution Functions, Measurability and Comonotonicity of Functions. 5. The Asymmetric Integral. 6. The Subadditivity Theorem. 7. The Symmetric Integral. 8. Sequences of Functions and Convergence Theorems. 9. Nullfunctions and the Lebesgue Spaces Lp. 10. Families of Measures and their Envelopes. 11. Densities and the Radon-Nikodym Theorem. 12. Products. 13. Representing Functionals as Integrals. References. Index.
1,327 citations