Stochastic Volatility

Stochastic volatility models aim to capture the fluctuating nature of asset price volatility, a key challenge in financial modeling and forecasting. Current research focuses on improving volatility prediction accuracy using both traditional econometric models like GARCH and advanced machine learning techniques, including recurrent neural networks (RNNs), deep reinforcement learning (DRL), and Gaussian processes (GPs), often in hybrid approaches combining both. These advancements have implications for risk management, option pricing, and portfolio optimization, offering more robust and accurate predictions than previously possible.