Covariance Matrix Estimation Under Positivity Constraints With Application to Portfolio Selection

• In this letter we propose a new method to estimate the covariance matrix under the constraint that its off-diagonal elements are non-negative, which has applications to portfolio selection in finance. We incorporate the non-negativity constraint in the maximum likelihood (ML) estimation problem and propose an algorithm based on the block coordinate descent method to solve for the ML estimate. To study the effectiveness of the proposed algorithm, we perform numerical simulations on both synthetic and real-world financial data, and show that our proposed method has better performance than that of a state-of-the-art method.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)

Keyword

Block coordinate descent

global minimum variance portfolio

maximum-likelihood estimation

non-negative correlations

portfolio selection

Publication and Content Type

ref (subject category)

art (subject category)