Universidad Adolfo Ibañez

Corruption and innovation in private firms: Does gender matter?

Área de publicación Finanzas
Tipo de publicación Artículos
Fecha de publicación 2020
Autores Viviana Fernández - Nirosha HewaWellalage; Sujani Thrikawala

We investigate the performance of machine-learning software and hardware for quantitative economics. We show that the use of machine-learning software and hardware can significantly reduce computational time in compute-intensive tasks. Using a sovereign default model and the Least Squares Monte Carlo option pricing algorithm as benchmarks, we show that specialized hardware and software speed up calculations by up to four orders of magnitude when compared to programs written in popular high-level programming languages, such as MATLAB, Julia, Python/Numpy, and R, and high-performing low-level languages such as C++.

Ver publicación
Redes Sociales