BIDS Data Science Lecture Series | February 26, 2016 | 1:10-2:30 p.m. | 190 Doe Library, UC Berkeley
The convergence of high-performance computing technologies, advances in data science, and increased data availability is materially impacting finance, as other domains. This discussion will survey the development of quantitative finance and the issue of characterizing asset returns and risks. In this context, we will explore the limitations of traditional mean-variance approaches and how to address these limitations for multi-asset class portfolios through the use of factor modeling and stochastic simulations boosted by high-performance computing. We introduce specific experimental architectures and approaches as well as data visualization techniques developed to provide a contemporary empirical framework for financial portfolio modeling.
