Black Scholes

The Black-Scholes model, a cornerstone of financial mathematics, aims to price options by modeling the underlying asset's price dynamics. Current research focuses on improving the model's accuracy and applicability, particularly for American options with early exercise features, by incorporating machine learning techniques such as neural networks (including convolutional and tensor networks), deep reinforcement learning, and gradient boosting methods, often combined with Monte Carlo simulations. These advancements address limitations of the original Black-Scholes model, particularly its assumptions about asset price behavior, leading to more robust and accurate option pricing and hedging strategies in practice.