On Portfolio Allocation: A Comparison of Using Low-Frequency and High-Frequency Financial Data

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 there is always a debate on when and how do we gain from using high-frequency data in portfolio optimization. This paper starts with a review on portfolio allocation and high-frequency financial time series. Then 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 by comparing the results of both low-frequency and high-frequency price data on stocks traded in New York Stock Exchange over a period of 209 days in 2010. The numerical 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.