<![CDATA[Market volatility poses significant challenges to investors and portfolio managers, particularly in periods of uncertainty where asset prices fluctuate sharply. Traditional static hedging strategies often fail to adapt effectively to dynamic market conditions, leading to suboptimal risk mitigation. This study proposes an Adaptive Hedging Strategy (AHS) that integrates real-time volatility estimation and machine learning–based signal processing to optimize hedge ratios dynamically. Using a dataset of equity index futures and options from 2015 to 2024, the research evaluates the performance of AHS against conventional delta and minimum-variance hedging approaches. Results indicate that the adaptive model significantly reduces portfolio variance and Value-at-Risk (VaR), particularly during high-volatility regimes such as the COVID-19 crash and post-2022 inflation shocks. The findings suggest that adaptive strategies can enhance hedging efficiency and provide a more resilient framework for risk management in volatile markets.]]>
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