  All the financial practitioners are working in incomplete markets full of
unhedgeable risk-factors. Making the situation worse, they are only equipped
with the 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) the
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 the standard Monte Carlo
simulation. The presented framework may also be useful for manufactures and
energy firms to install an efficient overlay of dynamic hedging by financial
derivatives to minimize the costs.
