Mathematical Modeling of Option Pricing with an Extended Black-Scholes Framework
This is an incremental improvement for financial modeling, specifically for pricing Google stock options.
This study tackled option pricing by extending the Black-Scholes model with stochastic volatility and interest rates, and comparing it to an LSTM model, finding that the LSTM had higher predictive accuracy while the finite difference method was more computationally efficient.
This study investigates enhancing option pricing by extending the Black-Scholes model to include stochastic volatility and interest rate variability within the Partial Differential Equation (PDE). The PDE is solved using the finite difference method. The extended Black-Scholes model and a machine learning-based LSTM model are developed and evaluated for pricing Google stock options. Both models were backtested using historical market data. While the LSTM model exhibited higher predictive accuracy, the finite difference method demonstrated superior computational efficiency. This work provides insights into model performance under varying market conditions and emphasizes the potential of hybrid approaches for robust financial modeling.