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Paper / 2025SMQ / 164822382248767
A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs
Dong Yan / Junyi Guo / Nanyi Zhang
deep learning / liquidity riskResource overview

A deep learning-driven iterative scheme for high-dimensional HJB equations in portfolio selection with exogenous and endogenous costs

Paper2025

by Dong Yan, Junyi Guo, Nanyi Zhang

Tags
deep learningliquidity risknumerical methodsoptimizationpartial differential equationsportfolio optimizationtransaction costs

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

Before you read

  1. Vectors, Matrices and Linear Systems

    Read the linear algebra used to define a numerical problem.

  2. Constrained and Convex Optimisation

    Read objectives, constraints and optimality conditions.

  3. Eigenvalues and Covariance Matrices

    Understand the covariance structure of portfolio risk.

Go further

  1. Calibration as an Inverse Problem

    Understand identifiability and fitted model parameters.

  2. Numerical Conditioning and Stability

    Separate model error from unstable numerical computation.

  3. Mean-Variance Optimisation

    Construct allocations and examine sensitivity to estimated inputs.

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