DYNAMIC MEAN–VARIANCE OPTIMIZATION PROBLEMS WITH DETERMINISTIC INFORMATION

Abstract

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 [Formula: see text] is a semimartingale, but not adapted to the filtration [Formula: see text] which models the information available for constructing trading strategies. We choose as [Formula: see text] the zero-information filtration and assume that [Formula: see text] is a time-dependent affine transformation of a square-integrable martingale. This class of processes includes in particular arithmetic and exponential Lévy models with suitable integrability. We give explicit solutions to the MVH and MVPS problems in this setting, and we show for the Lévy case how they can be expressed in terms of the Lévy triplet. Explicit formulas are obtained for hedging European call options in the Bachelier and Black–Scholes models.