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Maximum Lebesgue Extension Of Convex Risk Measures

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  • Keita Owari

    (Graduate School of Economics, University of Tokyo, Tokyo)

Abstract

Given a convex risk measure on $L^\infty$ having the Lebesgue property, we construct a solid space of random variables on which the original risk measure is extended preserving the Lebesgue property (on the entire space). This space is an order-continuous Banach lattice, and is maximum among all solid spaces admitting such a regular extension. We then characterize the space in terms of uniform integrability of certain families. As a byproduct, we present a generalization of Jouini-Schachermayer-Touzi’s theorem on the weakcompactness characterization of Lebesgue property, which is valid for any solid vector spaces of random variables, and does not require any topological property of the space.

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File URL: http://www.carf.e.u-tokyo.ac.jp/pdf/workingpaper/fseries/299.pdf
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Bibliographic Info

Paper provided by Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo in its series CARF F-Series with number CARF-F-287.

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Length: 26 pages
Date of creation: Aug 2012
Date of revision:
Handle: RePEc:cfi:fseres:cf287

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  1. Philippe Artzner & Freddy Delbaen & Jean-Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228.
  2. Patrick Cheridito & Tianhui Li, 2009. "Risk Measures On Orlicz Hearts," Mathematical Finance, Wiley Blackwell, vol. 19(2), pages 189-214.
  3. Patrick Cheridito & Freddy Delbaen & Michael Kupper, 2005. "Coherent and convex monetary risk measures for unbounded càdlàg processes," Finance and Stochastics, Springer, vol. 9(3), pages 369-387, 07.
  4. Freddy Delbaen, 2009. "Risk Measures For Non-Integrable Random Variables," Mathematical Finance, Wiley Blackwell, vol. 19(2), pages 329-333.
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