Data-Driven Dynamic Portfolio Selection with Vine Copula Simulation and Reinforcement Learning

Paper: arXiv 2610.20037

Authors: Gabriël M. J. Gelissen, Fengyi Yuan

Abstract

We develop a data-driven approach to dynamic portfolio selection that combines vine-copula simulation with reinforcement learning. The investor has constant relative risk aversion and maximises expected terminal utility with a running penalty on the conditional value-at-risk (CVaR) of one-period losses. We estimate a conditional vine model from historical returns and use it to generate joint scenarios for CVaR evaluation and conditional paths for training a recurrent portfolio policy. Under uniform local derivative bounds, we propagate edgewise pair-copula parameter errors through the conditional transforms to obtain power-law stability bounds in Hellinger distance and total variation. In a finite-horizon investment model, we combine these estimates with a marginal and structural approximation budget to bound policy values, optimal values and the true-market loss of policies selected under a fitted model, with separate simulation and numerical optimisation terms. On a seven-asset panel comprising US and Chinese equities and gold, the selected ensemble of ten trained policies achieves a compound annual growth rate of 34.13% and a Sharpe ratio of 2.31 over 22 complete monthly holding periods, exceeding the corresponding values of six classical and vine-based benchmarks. Combining simulated and historical training data reduces monthly target-to-target turnover by 41.5% relative to training on historical data alone, while maintaining comparable growth.

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: This paper presents a highly sophisticated mathematical framework for dynamic portfolio optimization, combining advanced statistical modeling with reinforcement learning. The empirical validation is robust, demonstrating strong performance against benchmarks and providing valuable insights into the benefits of synthetic data. The theoretical contributions regarding error propagation in vine copulas are particularly novel and significant.

Research Flowchart

  flowchart TD
    A[Research Goal: Dynamic Portfolio Selection for Max Terminal Utility with CVaR Penalty] --> B{Methodology: Vine-Copula Simulation & Reinforcement Learning}
    B --> C[Data/Inputs: Historical Asset Returns]
    C --> D{Computational Process: Model Estimation & Policy Training}
    D --> E[Outcomes: High CAGR, Sharpe Ratio, Reduced Turnover]