Paper: arXiv 2501.15793

Abstract

This study presents the Adaptive Minimum-Variance Portfolio (AMVP) framework and the Adaptive Minimum-Risk Rate (AMRR) metric, innovative tools designed to optimize portfolios dynamically in volatile and nonstationary financial markets. Unlike traditional minimum-variance approaches, the AMVP framework incorporates real-time adaptability through advanced econometric models, including ARFIMA-FIGARCH processes and non-Gaussian innovations. Empirical applications on cryptocurrency and equity markets demonstrate the proposed framework’s superior performance in risk reduction and portfolio stability, particularly during periods of structural market breaks and heightened volatility. The findings highlight the practical implications of using the AMVP and AMRR methodologies to address modern investment challenges, offering actionable insights for portfolio managers navigating uncertain and rapidly changing market conditions.

Complexity vs Empirical Score

  • Math Complexity: 8.5/10
  • Empirical Rigor: 6.0/10 (capped from 7.0: transaction costs not modelled; see penalty flags)
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced econometric models like ARFIMA-FIGARCH and non-Gaussian distributions, indicating high mathematical sophistication, while its empirical applications on real cryptocurrency and equity markets with specific datasets and performance metrics demonstrate significant backtest-ready rigor.

Research Flowchart

  flowchart TD
  A["Research Goal: Dynamic Portfolio Optimization<br>in Nonstationary Markets"] --> B["Methodology: AMVP & AMRR Frameworks<br>with ARFIMA-FIGARCH Modeling"]
  B --> C["Data Inputs:<br>Equity & Cryptocurrency Markets"]
  C --> D["Computational Process:<br>Real-time Adaptation & Non-Gaussian Inference"]
  D --> E["Outcome: Superior Risk Reduction<br>& Portfolio Stability"]

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1 of 31 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.