Large Portfolio Allocation Using High-Frequency Financial Data

• Activity Number: 347
• Type: Topic Contributed
• Date/Time: Tuesday, August 6, 2013 : 10:30 AM to 12:20 PM
• Sponsor: Business and Economic Statistics Section
• Author(s): Jian Zou*+ and Yichao Wu
• Companies: Indiana University-Purdue University Indianapolis and NC State University
• Keywords: Portfolio allocation ; risk management ; volatility matrix estimation ; high-frequency data ; regularization

Abstract

Portfolio allocation is one of the most fundamental problems in finance. The process of determining the optimal mix of assets to hold in the portfolio is a very important issue in risk management. It involves dividing an investment portfolio among different assets based on the volatilities of the asset returns. In the recent decades, it gains popularity to estimate volatilities of asset returns based on high-frequency data in financial economics. However the most available methods are not directly applicable when the number of assets involved is large, since small component-wise estimation errors could accumulate to large matrix-wise errors. In this talk, we introduce a new methodology to carry out efficient asset allocations using regularization on estimated integrated volatility via intra-day high-frequency data. We illustrate the methodology with the high-frequency price data on stocks traded in New York Stock Exchange over a period of 209 days in 2010. The theory and numerical results show that our approach perform well in portfolio allocation by pooling together the strengths of regularization and estimation from a high-frequency finance perspective.