Volatility Index

Volatility indices, such as the VIX, quantify market uncertainty by measuring the implied volatility of options contracts. Current research focuses on improving the accuracy of VIX forecasting using advanced machine learning techniques, including deep learning architectures like Temporal Convolutional Networks (TCNs) and hybrid models combining classical methods with neural networks, often incorporating Bayesian approaches for improved uncertainty quantification. These advancements aim to enhance risk management in financial markets and improve the performance of quantitative trading strategies by providing more reliable predictions of market volatility. Furthermore, research explores using textual data, such as news articles, to construct alternative volatility indices and improve predictive power.