Path Dependent Option

Path-dependent options, whose value depends on the entire price path of the underlying asset rather than just the final price, present significant challenges in pricing and hedging. Current research focuses on developing efficient algorithms, including those leveraging quantum computing and reinforcement learning, to overcome these challenges, with particular attention paid to improving the accuracy and speed of pricing models using techniques like tensor networks and deep signature methods. These advancements are crucial for accurately valuing complex financial instruments and for developing robust risk-management strategies in financial markets. The development of more efficient and accurate pricing models has significant implications for both theoretical finance and practical applications in risk management and algorithmic trading.