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Activity Number: 604 - Recent Advances in High-Frequency and High-Dimensional Time Series
Type: Topic Contributed
Date/Time: Thursday, August 3, 2017 : 8:30 AM to 10:20 AM
Sponsor: Business and Economic Statistics Section
Abstract #323784
Title: Efficient Portfolio Allocation with Sparse Volatility Estimation for High-Frequency Financial Data
Author(s): Jian Zou*
Companies: Worcester Polytechnic Institute
Keywords: portfolio allocation ; volatility estimator ; high-frequency data

Traditionally, investors try to estimate short term portfolio volatility based on daily return. When tick-by-tick data are available, investors use different volatility estimators based on high-frequency data to evaluate the portfolio risk in the hope of outperforming those based on low-frequency data. In this paper, we optimize block realized kernel estimator and propose another more efficient way when we deal with the large portfolio allocation. Our research contribution focuses on the benefits of high-frequency data for portfolio allocation based on sparsity induced volatility estimation methods. This process provides us new insights and alternatives when we want to set up a sensible investment strategy especially for those risk averse investors.

Authors who are presenting talks have a * after their name.

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