High dimensional minimum risk portfolio optimization

Liusha Yang (Department of Electronic and Computer Engineering, Hong Kong University of Science and Technology)
June 26, 2015 — 10:30 — "None"

Abstract

The performance of the global minimum risk portfolio (GMVP) relies on the accuracy of the estimated covariance matrix of the portfolio asset returns. For large portfolios, the number of available market returns is often of similar order to the number of assets, making the sample covariance matrix performs poorly. In this talk, we discuss two newly- developed GMVP optimization strategies under high dimensional analysis. The first approach is based on the shrinkage Tyler’s robust M-estimation with a risk-minimizing shrinkage parameter. It not only deals with the problem of sample insufficiency, but also the impulsiveness of financial data. The second approach is built upon a spiked covariance model, by assuming the population covariance matrix follows the spiked covariance model, in which several eigenvalues are significantly larger than all the others, which all equal one. The performances of our strategies will be demonstrated through synthetic and real data simulations.

Biography

Liusha Yang received the B.S. in Communication Engineering from the Beijing University of Posts and Telecommunications in 2012. Currently, she is a Ph.D. student in the Department of Electronic and Computer Engineering at the Hong Kong University of Science and Technology. Her research interests include random matrix theory and signal processing, with applications in financial engineering.