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

Paper: arXiv 2509.03712

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.

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 6.0/10 (capped from 8.0: transaction costs not modelled; no out-of-sample test; see penalty flags)
• Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: 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.

Research Flowchart

  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"]

Cited References (29)

1. NUAM mercado único (2025) — link
2. A comparative study of hierarchical risk parity portfolio and eigen portfolio on the NIFTY 50 stocks (2022) — link
3. An empirical evaluation of distance metrics in hierarchical risk parity methods for asset allocation : Distance metrics in hierarchical risk parity methods … (2025) — link
4. Risk parity models for portfolio optimization : A study of the Toronto Stock Exchange (2019) — link
5. Building diversified portfolios that outperform out of sample (2016) — link
6. Using deep reinforcement learning with hierarchical risk parity for portfolio optimization (2023) — link
7. Foundations of portfolio theory (1991) — link
8. The Markowitz portfolio theory

0 of 29 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.