Advancing Portfolio Optimization: Adaptive Minimum-Variance Portfolios and Minimum Risk Rate Frameworks

29 Pages Posted: 24 Feb 2025

See all articles by Ayush Jha

Ayush Jha

Texas Tech University

Abootaleb Shirvani

Kean University

Ali M. Jaffri

Texas Tech University

Svetlozar T. Rachev

Texas Tech University

Frank J. Fabozzi

Johns Hopkins University - Carey Business School

Date Written: January 26, 2025

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.

Keywords: Shadow Riskless Rate, Adaptive Minimum-Variance Portfolio, Adaptive Minimum-Risk Rate, Long-Range Depedence, Asset Pricing, Risk Premia

Suggested Citation

Jha, Ayush and Shirvani, Abootaleb and Jaffri, Ali M. and Rachev, Svetlozar T. and Fabozzi, Frank J., Advancing Portfolio Optimization: Adaptive Minimum-Variance Portfolios and Minimum Risk Rate Frameworks (January 26, 2025). Available at SSRN: https://ssrn.com/abstract=5112523 or http://dx.doi.org/10.2139/ssrn.5112523

Ayush Jha (Contact Author)

Texas Tech University ( email )

2500 Broadway
Lubbock, TX 79409
United States

Abootaleb Shirvani

Kean University ( email )

1000 Morris Ave
Union, NJ 07083
United States

Ali M. Jaffri

Texas Tech University ( email )

Svetlozar T. Rachev

Texas Tech University ( email )

2500 Broadway
Lubbock, TX 79409
United States

Frank J. Fabozzi

Johns Hopkins University - Carey Business School ( email )

100 International Drive
Baltimore, MD 21202
United States

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