TY - GEN
T1 - Online robust portfolio risk management using total least-squares and parallel splitting algorithms
AU - Slavakis, Konstantinos
AU - Leus, Geert
AU - Giannakis, Georgios B
PY - 2013/10/18
Y1 - 2013/10/18
N2 - The present paper introduces a novel online asset allocation strategy which accounts for the sensitivity of Markowitz-inspired portfolios to low-quality estimates of the mean and the correlation matrix of stock returns. The proposed methodology builds upon the total least-squares (TLS) criterion regularized with sparsity attributes, and the ability to incorporate additional convex constraints on the portfolio vector. To solve such an optimization task, the present paper draws from the rich family of splitting algorithms to construct a novel online splitting algorithm with computational complexity that scales linearly with the number of unknowns. Real-world financial data are utilized to demonstrate the potential of the proposed technique.
AB - The present paper introduces a novel online asset allocation strategy which accounts for the sensitivity of Markowitz-inspired portfolios to low-quality estimates of the mean and the correlation matrix of stock returns. The proposed methodology builds upon the total least-squares (TLS) criterion regularized with sparsity attributes, and the ability to incorporate additional convex constraints on the portfolio vector. To solve such an optimization task, the present paper draws from the rich family of splitting algorithms to construct a novel online splitting algorithm with computational complexity that scales linearly with the number of unknowns. Real-world financial data are utilized to demonstrate the potential of the proposed technique.
KW - Markowitz portfolio
KW - projection
KW - proximal mapping
KW - sparsity
KW - splitting algorithms
KW - total least-squares
UR - http://www.scopus.com/inward/record.url?scp=84890526528&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84890526528&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2013.6638753
DO - 10.1109/ICASSP.2013.6638753
M3 - Conference contribution
AN - SCOPUS:84890526528
SN - 9781479903566
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 5686
EP - 5690
BT - 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
T2 - 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
Y2 - 26 May 2013 through 31 May 2013
ER -