  Theoretical models applied to option pricing should take into account the
empirical characteristics of the underlying financial time series. In this
paper, we show how to price basket options when assets follow a shifted
log-normal process with jumps capable of accommodating negative skewness. Our
technique is based on the Hermite polynomial expansion that can match exactly
the first m moments of the model implied-probability distribution. This method
is shown to provide superior results for basket options not only with respect
to pricing but also for hedging.
