Optimal hedging for fund and insurance managers with partially observable investment flows

All financial practitioners are working in incomplete markets full of unhedgeable risk factors. Making the situation worse, they are only equipped with imperfect information on the relevant processes. In addition to the market risk, fund and insurance managers have to be prepared for sudden and possibly contagious changes in the investment flows from their clients so that they can avoid the over- as well as under-hedging. In this work, the prices of securities, the occurrences of insured events and (possibly a network of) investment flows are used to infer their drifts and intensities by a stochastic filtering technique. We utilize the inferred information to provide the optimal hedging strategy based on the mean-variance (or quadratic) risk criterion. A BSDE approach allows a systematic derivation of the optimal strategy, which is shown to be implementable by a set of simple ODEs and standard Monte Carlo simulation. The presented framework may also be useful for manufacturers and energy firms to install an efficient overlay of dynamic hedging by financial derivatives to minimize the costs.