DYNAMIC INDIFFERENCE VALUATION VIA CONVEX RISK MEASURES

By Susanne Klöppel, Martin Schweizer. 2007

181 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/78194cadf3ab3ddf98934ae69b8e619031fd4c00

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Citations in the corpus

CASH SUBADDITIVE RISK MEASURES AND INTEREST RATE AMBIGUITY (2007, background): “In particular, following Peng (2004), El Karoui, Peng, and Quenez (1997), Barrieu and El Karoui (2006) and Rosazza Gianin (2006) who link backward stochastic differential equations (BSDEs) and risk measures, we show that BSDEs with suitable coefficients are cash sub-additive risk measures.”
Risk measuring under model uncertainty (2010, background): “Dynamic risk measures have then been studied in many papers, among them [7, 8, 11, 13, 25, 30].”
Recent advances in reinforcement learning in finance (2021, unknown): “By contrast, [31] used deep neural networks to approximate an optimal hedging strategy under market frictions, including transaction costs, and convex risk measures [131] such as conditional value at risk.”
Convex risk measures and the dynamics of their penalty functions (2006, methodology): “[6], Burgert [5], and Klöppel and Schweizer [16].”
To Split or Not to Split: Capital Allocation with Convex Risk Measures (2007, unknown): “(2007)) or tools for pricing in incomplete markets (Klöppel and Schweizer, 2007).”
Risk-averse dynamic programming for Markov decision processes (2010, background): “Much work on dynamic measures of risk focused on the case when we have just one final cost ZT and we are evaluating it from the perspective of earlier stages t ; see, inter alia [14,18,28,48].”
A control approach to robust utility maximization with logarithmic utility and time-consistent penalties (2007, background): “Robust utility maximization is also closely related to other optimization problems involving convex and coherent risk measures, and these problems have also received a lot of attention recently; see, for instance, Barrieu and El Karoui [3], Klöppel and Schweizer [16], Gundel and Weber [13], and the”
Lectures on Stochastic Programming: Modeling and Theory (2009, background): “In the finance literature time consistency is usually defined via the composition property of dynamic risk measures; see, Artzner, Delbaen, Eber, Heath and Ku [9], BionNadal [20, 21], Cheridito, Delbaen and Kupper [38], Cheridito and Kupper [39], Föllmer and Penner [79], Frittelli and Scandolo [81],”
A general theory of finite state Backward Stochastic Difference Equations (2008, methodology): “Similar ideas are used in [10], where E is assumed to be concave, and is referred to as a ‘monetary concave utility functional’.”
OPTIMAL INVESTMENT WITH AN UNBOUNDED RANDOM ENDOWMENT AND UTILITY‐BASED PRICING (2007, background): “Recently, Mania and Schweizer (2005) have studied a dynamic version of indifference prices for exponential utility, and Klöppel and Schweizer (2007) consider dynamic versions of utility indifference prices via convex risk measures.”
OPTIMAL RISK SHARING FOR LAW INVARIANT MONETARY UTILITY FUNCTIONS (2008, background): “The equivalences (ii)–(v) are known duality results, even in a more general dynamic context, see Klöppel and Schweizer (2007).”
VALUATIONS AND DYNAMIC CONVEX RISK MEASURES (2007, methodology): “…statement of various results rather clumsy and non-intuitive; in common with others (for example, Artzner et al. 2007; Cheridito et al. 2004, 2006; Klöppel and Schweizer 2007), we shall instead speak of a valuation,2 1 See Deprez and Gerber (1985), Artzner et al. (1997, 1999), Delbaen (2000,…”