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印第安纳大学与普渡大学印第安纳波里斯联合分校Jian (Frank) Zou学术报告

ag视讯:2014-06-20 编辑: 来源:



Title High Performance Computations for Statistical Methods in High-Frequency Financial Statistics

Time: 15:30, June 20, 2014

Venue: Meeting Room on the 2nd floor, 3-B Building, Software Campus

Speaker: Dr. Jian (Frank) Zou

Host:  Prof. Xin-Shun Xu


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 (such as LASSO and SCAD) 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 compare the results of both low-frequency and 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. Using a combination of software and hardware parallelism, we demonstrate a high level of performance on high-frequency financial statistics.


Jian (Frank) Zou is currently Assistant Professor of Statistics at Indiana University-Purdue University Indianapolis (IUPUI) and Worcester Polytechnic Institute (WPI). He got his Ph.D. of Statistics at University of Connecticut in 2009. He was a postdoc at the National Institute of Statistical Sciences (NISS) and Duke University from 2009-2011. His research interests include financial time series, spatial statistics, biosurveillance, statistical computing and Bayesian statistics.



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