Decomposing Volatility: Continuous and Jump Component
Illustrating how realized volatility can decomposed into a continuous and jump component, providing new modelling opportunities.
Volatility
Stochastic Volatility Models in Stan
Fitting 2 different Stochastic Volatility Models to S&P 500 returns and finding out which is better
Mixture Density Network for Forecasting Realized Volatility
Using a mixture density neural network implemented in PyTorch to forecast the distribution of future realized volatility.
Bayesian Autoregressive Volatility Forecasting
Using a simple bayesian autoregressive model to forecast future volatility
Comparison of Volatility Estimators
Comparing Garman-Klass estimator to 5-minute Realized Volatility estimator.