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Paper / 2023SMQ / 79085603218092
Combining Reinforcement Learning and Barrier Functions for Adaptive Risk Management in Portfolio Optimization
Hejun Huang / Vincent Tam / Zhenglong Li
machine learning / portfolio optimizationResource overview

Combining Reinforcement Learning and Barrier Functions for Adaptive Risk Management in Portfolio Optimization

Paper2023

by Hejun Huang, Vincent Tam, Zhenglong Li

Tags
machine learningportfolio optimizationreinforcement learningrisk management

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Understand this paper

Suggested foundations for portfolio construction and risk measurement and stress. 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

    Read objectives, constraints and optimality conditions.

  3. Random Variables and Distributions

    Represent losses as a distribution rather than one forecast.

  4. Expected Shortfall and Other Tail Risk Measures

    Distinguish a loss quantile from the average loss in the tail.

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. VaR Estimation in Practice: Variance-Covariance, Historical Simulation, Monte Carlo

    Compare risk estimators and their assumptions.

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