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Beyond GMV: the relevance of covariance matrix estimation for risk-based portfolio construction
Clint Howard / Harald Lohre / M. Sipke Dom / Maarten Jansen
optimization / portfolio optimizationResource overview

Beyond GMV: the relevance of covariance matrix estimation for risk-based portfolio construction

Paper2025

by Clint Howard, Harald Lohre, M. Sipke Dom, Maarten Jansen

Tags
optimizationportfolio optimization

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Suggested foundations for portfolio construction and optimisation and numerical methods. Specialist prerequisites vary by paper.

Before you read

  1. Eigenvalues and Covariance Matrices

    Understand the covariance structure of portfolio risk.

  2. Constrained and Convex Optimisation

    Identify the objective, constraints and conditions for an optimum.

  3. Vectors, Matrices and Linear Systems

    Read the linear algebra used to define a numerical problem.

Go further

  1. Mean-Variance Optimisation

    Construct allocations and examine sensitivity to estimated inputs.

  2. Portfolio Constraints

    Turn mathematical weights into feasible portfolio decisions.

  3. Calibration as an Inverse Problem

    Understand identifiability and fitted model parameters.

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Explore Portfolio Theory and Quantitative Investment Explore Linear Algebra, Optimisation and Numerical Methods

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