Hierarchical Risk Parity for Portfolio Allocation in the Latin American NUAM Market

ArXiv ID: 2509.03712 “View on arXiv”

Authors: Gonzalo Ramirez-Carrillo, David Ortiz-Mora, Alex Aguilar-Larrotta

Abstract

This study applies the Hierarchical Risk Parity (HRP) portfolio allocation methodology to the NUAM market, a regional holding that integrates the markets of Chile, Colombia and Peru. As one of the first empirical analyses of HRP in this newly formed Latin American context, the paper addresses a gap in the literature on portfolio construction under cross-border, emerging market conditions. HRP leverages hierarchical clustering and recursive bisection to allocate risk in a manner that is both interpretable and robust–avoiding the need to invert the covariance matrix, a common limitation in the traditional mean-variance optimization. Using daily data from 54 constituent stocks of the MSCI NUAM Index from 2019 to 2025, we compare the performance of HRP against two standard benchmarks: an equally weighted portfolio (1/N) and a maximum Sharpe ratio portfolio. Results show that while the Max Sharpe portfolio yields the highest return, the HRP portfolio delivers a smoother risk-return profile, with lower drawdowns and tracking error. These findings highlight HRP’s potential as a practical and resilient asset allocation framework for investors operating in the integrated, high-volatility markets like NUAM.

Keywords: Hierarchical Risk Parity (HRP), Portfolio Optimization, Hierarchical Clustering, Emerging Markets, Risk Allocation, Equities

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper applies advanced non-parametric math (hierarchical clustering, recursive bisection) to avoid matrix inversion, indicating high math complexity. It is highly empirically rigorous, using daily data on 54 stocks from 2019–2025 to benchmark HRP against standard portfolios with multiple performance metrics, demonstrating backtest-ready implementation.

  flowchart TD
    A["Research Goal<br/>Apply HRP to NUAM Market<br/>Identify Optimal Allocation"] --> B["Data & Inputs<br/>Daily Returns: 54 MSCI NUAM Stocks<br/>(2019-2025)"]
    B --> C{"Key Methodology: Hierarchical Risk Parity"}
    C --> D["Step 1: Compute Correlation<br/>Distance Matrix"]
    D --> E["Step 2: Hierarchical Clustering<br/>(Linkage Function)"]
    E --> F["Step 3: Recursive Bisection<br/>Top-Down Risk Allocation"]
    C --> G["Benchmarks: Equally Weighted<br/>& Max Sharpe Ratio Portfolios"]
    F --> H["Key Findings & Outcomes"]
    G --> H
    H --> I["HRP delivers smoother<br/>risk-return profile"]
    H --> J["Lower Drawdowns<br/>& Tracking Error vs. Max Sharpe"]
    H --> K["Robust method for<br/>emerging market integration"]