Varying Coefficient GARCH

Let's make GARCH have varying coefficients to handle non-linear conditional variance.

When Point Forecasts Are Completely Useless

Why I prefer Probabilistic Forecasts - Hitting Time Probabilities

Random Forests and Boosting for ARCH-like volatility forecasts

Forecasting with Decision Trees and Random Forests

Multivariate GARCH with Python and Tensorflow

Cointegrated time-series and when differencing might be bad

Varying Coefficient GARCH

Let's make GARCH have varying coefficients to handle non-linear conditional variance.

When Point Forecasts Are Completely Useless

Why I prefer Probabilistic Forecasts - Hitting Time Probabilities

Random Forests and Boosting for ARCH-like volatility forecasts

Forecasting with Decision Trees and Random Forests

Multivariate GARCH with Python and Tensorflow

Cointegrated time-series and when differencing might be bad

Bayesian Machine Learning and Julia are a match made in heaven

Bayesian inference with less pain and even less code.

When is Bayesian Machine Learning actually useful?

A Gaussian Process model for heteroscedasticity