Abstract

This paper addresses the problem of constructing optimal equity portfolios under volatile market conditions by minimizing realized volatility—an alternative risk quantifier that more accurately captures short-term market fluctuations than traditional variance-based approaches. This issue is particularly relevant for investors seeking robust risk management strategies in dynamic and uncertain environments. We propose a mathematical optimization framework that determines portfolio weights by minimizing realized volatility, subject to expected return constraints. The model is empirically validated using historical data from stocks listed in the Stock Exchange of Thailand 50 (SET50) index. Through a comparative analysis of realized volatility and variance-based optimization across multiple portfolio sizes and return levels, we find that portfolios constructed using realized volatility consistently achieve higher Sharpe ratios, indicating superior risk-adjusted performance. We further introduce an efficiency metric based on the Euclidean distance between optimal portfolio weight vectors to evaluate the stability of allocations under extended investment horizons. The findings underscore the practical advantages of realized volatility in portfolio construction, offering enhanced responsiveness to market dynamics and improved performance outcomes. The novelty of this study lies in integrating realized volatility into a constrained portfolio optimization model and empirically demonstrating its superiority, thereby extending traditional mean-variance methods in both scope and effectiveness.