ETD

• Deep Learning
• Econometrics
• Interpretability
• Machine Learning
• Realized Volatility
• XAI

Forecasting realized volatility has a central role in risk management, portfolio optimization, and derivative pricing. While traditional econometric models often struggle to capture the nonlinearities and complex interactions inherent in financial markets, on the other side recent advances in deep learning may produce stronger predictive performance, but their lack of interpretability limits practical adoption in finance.
This thesis implements an interpretable deep learning architecture for realized volatility forecasting that integrates not only historical volatility patterns but also a rich set of external predictors, including macroeconomic variables, technical indicators, and sentiment indicators obtained through NLP techniques. The proposed model leverages modern attention based interpretability techniques in order to improve transparency into the drivers of volatility dynamics while maintaining high predictive accuracy. Moreover, adoption of a stacking ensemble architecture with HAR model, a classical econometric model for volatility modeling, is able, in some cases, to improve forecasting accuracy still retaining overall interpretability of the system.
Empirical evaluation is conducted on European stock data, with comparisons against benchmark econometrics and machine learning models. The results indicate that the proposed model and architecture outperforms traditional baselines in forecast accuracy and yields valuable insights on relative importance of economic, technical, and sentiment-based predictors.
This research project contributes practically, by offering a transparent and robust tool for financial decision-making leveraging both deep learning architecture and classical econometrics models and by evaluating models on EU stock market.