Risk Management in Derivatives Markets: Integrating Advanced Hedging Strategies with Empirical Analysis

Abstract

This study provides a comprehensive analysis of hedging strategies in the Chinese 50ETF options market, emphasizing dynamic hedging, Value at Risk (VaR), and machine learning-based approaches. Each strategy is meticulously evaluated against various market conditions, revealing distinct strengths and weaknesses. Dynamic hedging proves cost-effective in stable markets but struggles in high volatility scenarios, limiting its risk reduction capacity. Conversely, the VaR model, while reducing risk effectively under extreme conditions, may lead to over-hedging in calmer markets due to high costs and dependence on historical data. Machine learning strategies excel in adapting to complex and nonlinear market dynamics, though their effectiveness is contingent on data accuracy and model robustness. The study highlights the criticality of aligning strategies with market specifics and investor risk profiles, advocating for adaptable risk management in the fluctuating realm of financial derivatives. It provides empirical insights into modern hedging methods, offering practical guidance for their application in dynamic financial markets.