Statistical methods for large portfolio risk management

Abstract

Portfolio allocation is one of the most important problems in financial 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 highfrequency data in financial economics. In this article, we focus on the portfolio allocation problem using high-frequency financial data. The 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 lowfrequency and high-frequency price data on stocks traded in the New York Stock Exchange in 2011. 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.