|
Abstract
|
We construct a time-consistent sublinear expectation (i.e. risk measure) in the setting of volatility uncertainty. This mapping extends Peng's G-expectation by allowing the range of the volatility uncertainty to be stochastic. Our construction is purely probabilistic and based on an optimal control formulation with path-dependent control sets.
In the second part of the talk, we consider a general class of sublinear expectations in this setting and discuss the associated nonlinear martingales and superhedging problems
(joint work with Mete Soner).
|