Option Forecasting

Option forecasting aims to predict the future price of financial options, a complex task due to market volatility and inherent uncertainties. Current research heavily utilizes machine learning, particularly neural networks (including convolutional and recurrent architectures like LSTMs) often in conjunction with established models like the Black-Scholes equation, sometimes employing techniques like the Quasi-Reversibility Method to address ill-posed inverse problems. These approaches seek to improve prediction accuracy and inform optimized trading strategies, potentially leading to more efficient and profitable investment decisions. The field's significance lies in its potential to refine financial modeling and risk management within the complex landscape of options markets.