Risk measures via g-expectations

By Emanuela Rosazza Gianin. 2006

317 citations recorded by the source corpus.

Subfields: Demography, Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/655f74ec66f6d472587424ff1c6836bb10b4a672

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Demography

In Finance

In Management Science and Operations Research

Citations in the corpus

Mean-Variance Policy for Discrete-time Cone Constrained Markets: The Consistency in Efficiency and Minimum-Variance Signed Supermartingale Measure (2014, unknown): “4 THE VARIANCE-OPTIMAL SIGNED SUPERMARTINGALE MEASURE It has been well known that the problems of mean-variance portfolio selection and mean-variance hedging have a strong connection (see Schweizer (2010)).”
CASH SUBADDITIVE RISK MEASURES AND INTEREST RATE AMBIGUITY (2007, background): “Coherent risk measures have been introduced by Artzner et al. (1997, 1999) and further developed by Delbaen (2000, 2002); sublinear risk measures by Frittelli (2000); convex risk measures by Föllmer and Schied (2002a), Föllmer and Schied (2002b), and Frittelli and Rosazza Gianin (2002).”
Reflected BSDEs when the obstacle is not right-continuous and optimal stopping (2015, unknown): “, for instance, [2], [32], [33], [35], [3] among others).”
Existence and Comparisons for BSDEs in general spaces (2010, background): “A further discussion of this relationship can be found in [21].”
A general theory of finite state Backward Stochastic Difference Equations (2008, background): “The connection between these is present in [1], and more generally in [14].”
Recent progress in random metric theory and its applications to conditional risk measures (2010, background): “Recently, motivated by the study of dynamic risk measures [58, 60] and conditional entropic risk measures, Filipović, Kupper and Vogelpoth studied the following convex conditional risk measures of LpF(E)-type and L -type in [15, 16, 56].”
Convexity, translation invariance and subadditivity for g-expectations and related risk measures (2008, methodology): “3 of [3] and helps us to confirm the same necessary and sufficient conditions in [3] and [21] without the additional continuity assumption (A4).”
DYNAMIC INDIFFERENCE VALUATION VIA CONVEX RISK MEASURES (2007, background): “Other related conditional representation results for convex risk measures can be found in Rosazza Gianin (2006) in the context of BSDEs.”
Comparisons for backward stochastic differential equations on Markov chains and related no-arbitrage conditions (2008, methodology): “This can be seen in [2], and more generally in [16].”
Error estimates of the $\theta$-scheme for backward stochastic differentialequations (2009, background): “The nonlinear g-expectation via a particular nonlinear BSDE was introduced in [27], and in [29] it was found that a dynamic coherent risk measure can be represented by a properly defined g-expectation.”
Backward Stochastic Differential Equation, Nonlinear Expectation and Their Applications (2011, methodology): “For a concave dynamic expectation with an assumption much weaker than the above domination condition, we can still find a function g = g(t, z) with possibly singular values, see Delbaen, Peng and Rosazza Gianin (2009).”
Dynamic risk measures (2010, unknown): “[32, 20, 38, 26].”
7 – Backward Stochastic Differential Equations (2011, unknown): “Gianin [36] showed that if g is sublinear, g-expectation corresponds to a coherent risk measure and if g is convex, g-expectation corresponds to a convex risk measure.”