ProfessorRichard Gerlach

Bayesian Parametric Financial Risk Forecasting Employing Multiple High-Frequency Realized Measures

Dynamic Quantile Forecasting with High-Frequency Data: A New Framework

Extending Bayesian Semi-Parametric Realized-CARE model for Tail Risk Forecasting Incorporating Multiple Realized Measures

Machine Learning Enhanced Risk Forecasting with High-Frequency Data

Semi-parametric financial risk forecasting via Realized-EGARCH incorporating multiple realized measures and Bayesian regularization techniques.

Stock Market Movement Prediction Using Machine Learning Techniques and Graph-based Approaches