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<p>This study evaluates the performance of portfolio optimization strategies tailored for environmentally responsible Exchange-Traded Funds (ETFs). Using a dataset of 37 green ETFs spanning the period from October 16, 2017, to March 8, 2024, three distinct portfolio construction methods are compared: a traditional Mean–Variance model, a volatility-adjusted GARCH model, and a hybrid MACD-KMeans-MeanVaR framework that integrates technical indicators with unsupervised learning. The empirical findings indicate that green ETFs yield competitive returns and exhibit strong risk-adjusted performance, particularly during periods of heightened market volatility. The GARCH-based portfolio consistently achieves the highest Sharpe ratios, while the clustering-based strategy excels in managing downside risk, as evidenced by its superior Sortino and Calmar ratios. Robustness is confirmed through Monte Carlo simulations and sensitivity analyses across multiple risk scenarios. All models outperform benchmark indices—including the NYSE Composite, S&amp;P 500, and NASDAQ Clean Edge Green Energy Index—on at least one key metric. These results reinforce the practicality of sustainable investing and highlight the potential of integrating quantitative techniques with ESG-aligned portfolio management.</p>

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Green Finance and Marketing: Portfolio Optimization Approaches for Environmentally Responsible ETFs

  • 期刊论文

  • 发表日期：

    2025年12月20日

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  • Melike Aktaş Bozkurt,
  • Şeyda Ok,
  • Serdar Çelik

摘要

This study evaluates the performance of portfolio optimization strategies tailored for environmentally responsible Exchange-Traded Funds (ETFs). Using a dataset of 37 green ETFs spanning the period from October 16, 2017, to March 8, 2024, three distinct portfolio construction methods are compared: a traditional Mean–Variance model, a volatility-adjusted GARCH model, and a hybrid MACD-KMeans-MeanVaR framework that integrates technical indicators with unsupervised learning. The empirical findings indicate that green ETFs yield competitive returns and exhibit strong risk-adjusted performance, particularly during periods of heightened market volatility. The GARCH-based portfolio consistently achieves the highest Sharpe ratios, while the clustering-based strategy excels in managing downside risk, as evidenced by its superior Sortino and Calmar ratios. Robustness is confirmed through Monte Carlo simulations and sensitivity analyses across multiple risk scenarios. All models outperform benchmark indices—including the NYSE Composite, S&P 500, and NASDAQ Clean Edge Green Energy Index—on at least one key metric. These results reinforce the practicality of sustainable investing and highlight the potential of integrating quantitative techniques with ESG-aligned portfolio management.