Volume-Weighted Golden Ratio Estimator (vGRE) for Drawdown and Tail-Risk Control for Wealth Portfolios

Title

Authors

LANZ CWJ CHAN
Finamatrix Quant-Lab, Singapore
WING-KEUNG WONG
Department of Finance, Quantum AI Research Center, Fintech & Blockchain Research Center, and Big Data Research Center, Asia University, Taiwan
Department of Medical Research, China Medical University Hospital, Taiwan
Business, Economic and Public Policy Research Centre, Hong Kong Shue Yan University
The Economic Growth Centre, Nanyang Technological University, Singapore
*Corresponding author

Abstract

Purpose – This paper introduces the Volume-Weighted Golden Ratio Estimator (vGRE) as a transparent, cybernetic overlay for dynamic drawdown and tail-risk control in wealth management portfolios, addressing limitations of traditional volatility- and VaR-based risk metrics that fail to protect high-net-worth and ultra-high-net-worth investors against sharp drawdowns and tail events.
Design/methodology/approach – The framework integrates golden-ratio-based segmentation of price swings with genetic optimization and volume-weighted confirmation to distinguish meaningful market moves from technical noise. Using 5-minute NAS100 index data from January 2024 to January 2026, we compare a systematic vGRE-based strategy against standard VWAP, RSI, Bollinger Bands, and a buy-and-hold benchmark through comprehensive backtesting and walk-forward validation.
Findings – The vGRE overlay achieves a Sharpe ratio of 2.34 compared with 1.76 for VWAP and 1.28 for buy-and-hold, while reducing maximum drawdown to 8.2% versus 14.5% and 22.7%, respectively. The system generates early warning signals 2–4 days ahead of the three largest drawdown episodes in the sample and reduces false-positive trading signals by 51% relative to an unweighted variant.
Practical implications – The vGRE framework offers wealth managers, family offices, and private banks a practical, implementable risk overlay that can sit atop existing strategic allocations, enhancing downside protection without requiring wholesale redesign of investment processes. The modular design enables integration into tactical risk budgeting, derivative hedging triggers, and investment committee decision protocols.
Originality/value – This study demonstrates that volume-weighted, cybernetic risk overlays combining golden-ratio structures with evolutionary optimization can materially improve drawdown control and tail-risk detection without requiring complex predictive models or black-box machine learning. The vGRE framework provides a theoretically grounded yet operationally accessible approach to adaptive risk management for private wealth portfolios.

Keywords

Wealth management; Drawdown control; Tail-risk; Volume-weighted indicators; Golden ratio; Risk cybernetics; Algorithmic overlay

Classification-JEL

G11, G17, G32, C44, C61, D81

Pages

1-23

How to Cite

Chan, L. C. W. J., & Wong, W.-K. (2026). Volume-Weighted Golden Ratio Estimator (vGRE) for Drawdown and Tail-Risk Control for Wealth Portfolios. International Journal of Wealth Management (IJWM), 2026(2), 1-23.