Almost two decades ago supercomputers and massively parallel computers promised to revolutionize the landscape of large-scale computing and provide breakthrough solutions in several application domains. Massively parallel processors achieve today terraFLOPS performance ± trillion ¯oating point operations per second ± and they deliver on their promise. However, the anticipated breakthroughs in application domains have been more subtle and gradual. They came about as a result of combined eorts with novel modeling techniques, algorithmic developments based on innovative mathematical theories, and the use of highperformance computers that vary from top-range workstations, to distributed networks of heterogeneous processors, and to massively parallel computers. An application that bene®ted substantially from high-performance computing is that of ®nance and ®nancial planning. The advent of supercomputing coincided with the so-called ``age of the quants'' in Wall Street, i.e., the mathematization of problems in ®nance and the strong reliance of ®nancial managers on quantitative analysts. These scientists, aided by mathematical models and computer simulations, aim at a better understanding of the peculiarities of the ®nancial markets and the development of models that deal proactively with the uncertainties prevalent in these markets. In this paper we give a modest synthesis of the developments of high-performance computing in ®nance. We focus on three major developments: (1) The use of Monte Carlo simulation methods for security pricing and Value-at-Risk (VaR) calculations; (2) the development of www.elsevier.com/locate/parco Parallel Computing 25 (1999) 2149±2175