Bayesian Model Averaging For Realized Volatility Models

This research explores statistical methods for forecasting realized volatility for stock market holdings; primarily Stochastic Dierential Equations for the development of various volatility measures and Bayesian Model Averaging for the development and optimization of a linear model capable of predicting said volatility. These methods will be outlined and explained before being applied to high frequency trade data.