High-Frequency Financial Statistics Through High-Performance Computing

Abstract

Financial statistics covers a wide array of applications in the financial world, such as (high-frequency) trading, risk management, pricing and valuation of securities and derivatives, and various business and economic analytics. Portfolio allocation is one of the most important problems in financial risk management. One most challenging part in portfolio allocation is the tremendous amount of data and the optimization procedures that require computing power beyond the currently available desktop systems. In this article, we focus on the portfolio allocation problem using high-frequency financial data, and propose a hybrid parallelization solution to carry out efficient asset allocations in a large portfolio via intra-day high-frequency data. We exploit a variety of HPC techniques, including parallel R, IntelⓇ Math Kernel Library, and automatic offloading to IntelⓇ Xeon Phi coprocessor in particular to speed up the simulation and optimization procedures in our statistical investigations. Our numerical studies are based on high-frequency price data on stocks traded in New York Stock Exchange in 2011. The analysis results show that portfolios constructed using high-frequency approach generally perform well by pooling together the strengths of regularization and estimation from a risk management perspective. We also investigate the computation aspects of large-scale multiple hypothesis testing for time series data. Using a combination of software and hardware parallelism, we demonstrate a high level of performance on high-frequency financial statistics.