Rough Volatility

Rough volatility models aim to capture the complex, irregular patterns observed in financial market volatility, improving prediction accuracy compared to traditional models. Current research focuses on developing and comparing advanced machine learning techniques, such as recurrent neural networks (RNNs, including LSTMs), neural stochastic differential equations (Neural SDEs), and transformer models, to forecast volatility across various asset classes and time scales. These efforts are driven by the need for more accurate risk management and improved trading strategies, with recent studies demonstrating that sophisticated deep learning approaches can outperform traditional econometric methods in certain contexts, particularly for high-frequency data and extreme volatility events.