Modern Frameworks for Quantitative Economics

Victor Fonseca Duarte, Diogo Duarte, Julia Fonseca Duarte, Alexis Montecinos

Research output: Working paper


We investigate the performance of machine learning software and hardware for quantitative economics. We show that the use of modern numerical frameworks can significantly reduce computational time in compute-intensive tasks. Using the Least Squares Monte Carlo option pricing algorithm as a benchmark, we show that specialized hardware and software speeds the calculations by up to two orders of magnitude when compared to programs written in popular high-level programming languages, such as Julia and Matlab.
Original languageEnglish (US)
Number of pages22
StateSubmitted - Jun 2019


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