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Accurately pricing complex financial instruments is a cornerstone of modern finance, essential for trading, investment strategy, and risk management. For many advanced models, especially those involving three or more stochastic variables like fluctuating interest rates or volatility, closed-form analytical solutions are impractical or impossible to derive. This reality makes numerical methods, particularly Monte-Carlo simulations, the only viable alternative for obtaining a s

To optimize a Field-Programmable Gate Array (FPGA) design, an Agent requires a formal mathematical model—either empirical (based on measurements) or analytical (based on first principles)—to guide the optimization. This model must allow the Agent to infer optimal actions, such as minimizing memory block transfers between storage and the processing pipeline. The paper reviewed addresses this by proposing the authors' FPGA Empirical Roofline (FER) benchmarking tool as a modelin

Given their unique capabilities, what surprising and counter-intuitive realities have kept FPGAs from becoming a mainstream force in HPC?

Background: The "Research Review" series is a grouping of articles that review scientific publications on the topics of financial modeling, foundation mathematics, and parallel computing. It is meant to enrich the community with technical knowledge and provide clarity on topics that can create distrust in the markets Paper being review: Ana M. Monteiro, António A.F. Santos, Parallel computing in finance for estimating risk-neutral densities through option prices Journal of Pa