DYNAMIC MEAN-VARIANCE OPTIMIZATION PROBLEMS WITH DETERMINISTIC INFORMATION

We solve the problems of mean-variance hedging (MVH) and mean-variance portfolio selection (MVPS) under restricted information. We work in a setting where the underlying price process S is a semimartingale, but not adapted to the filtration G which models the information available for constructing trading strategies. We choose as G = F-det the zero-information filtration and assume that S is a time-dependent affine transformation of a square-integrable martingale. This class of processes includes in particular arithmetic and exponential Levy models with suitable integrability. We give explicit solutions to the MVH and MVPS problems in this setting, and we show for the Levy case how they can be expressed in terms of the Levy triplet. Explicit formulas are obtained for hedging European call options in the Bachelier and Black-Scholes models.