Summary: The pricing of financial derivatives is an important field in finance and constitutes a major component of financial management applications. The uncertainty of future events often makes analytic approaches infeasible and, hence, time-consuming numerical simulations are required. In the Aurora Financial Management System, pricing is performed on the basis of lattice representations of stochastic multidimensional scenario processes using the Monte Carlo simulation and Backward Induction methods, the latter allowing for the exploitation of shared-memory parallelism. We present the parallelization of a Backward Induction numerical pricing kernel on a cluster of SMPs using HPF+, an extended version of High-Performance Fortran. Based on language extensions for specifying a hierarchical mapping of data onto an SMP cluster, the compiler generates a hybrid-parallel program combining distributed-memory and shared-memory parallelism. We outline the parallelization strategy adopted by the VFC compiler and present an experimental evaluation of the pricing kernel on an NEC SX-5 vector supercomputer and a Linux SMP cluster, comparing a pure MPI version to a hybrid-parallel MPI/OpenMP version.