Consistent Estimation of the High-Dimensional Efficient Frontier

ArXiv ID: 2409.15103 “View on arXiv”

Authors: Unknown

Abstract

In this paper, we analyze the asymptotic behavior of the main characteristics of the mean-variance efficient frontier employing random matrix theory. Our particular interest covers the case when the dimension $p$ and the sample size $n$ tend to infinity simultaneously and their ratio $p/n$ tends to a positive constant $c\in(0,1)$. We neither impose any distributional nor structural assumptions on the asset returns. For the developed theoretical framework, some regularity conditions, like the existence of the $4$th moments, are needed. It is shown that two out of three quantities of interest are biased and overestimated by their sample counterparts under the high-dimensional asymptotic regime. This becomes evident based on the asymptotic deterministic equivalents of the sample plug-in estimators. Using them we construct consistent estimators of the three characteristics of the efficient frontier. It it shown that the additive and/or the multiplicative biases of the sample estimates are solely functions of the concentration ratio $c$. Furthermore, the asymptotic normality of the considered estimators of the parameters of the efficient frontier is proved. Verifying the theoretical results based on an extensive simulation study we show that the proposed estimator for the efficient frontier is a valuable alternative to the sample estimator for high dimensional data. Finally, we present an empirical application, where we estimate the efficient frontier based on the stocks included in S&P 500 index.

Keywords: Random Matrix Theory, High-dimensional Asymptotics, Portfolio Optimization, Mean-Variance Efficient Frontier, Asymptotic Normality

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 6.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced random matrix theory and asymptotic analysis with extensive theoretical derivations, yielding high math complexity. It includes a simulation study and an empirical application on S&P 500 data, demonstrating practical implementation readiness, though the rigor is grounded more in theoretical asymptotics than live backtesting.

  flowchart TD
    A["Research Goal: Analyze asymptotic behavior of mean-variance efficient frontier in high dimensions"] --> B["Methodology: Random Matrix Theory RMT with p/n → c"]
    B --> C["Inputs: Asset returns no distributional assumptions p>n scenario"]
    C --> D["Computation: Derive deterministic equivalents for sample plug-in estimators"]
    D --> E["Computation: Construct consistent estimators correct for RMT bias"]
    E --> F["Validation: Prove asymptotic normality Monte Carlo simulations"]
    F --> G["Application: Empirical analysis on S&P 500 stocks"]
    G --> H["Findings: Two frontier quantities biased consistent estimators proposed RMT bias depends only on c"]