Varying Coefficient GARCH
Let's make GARCH have varying coefficients to handle non-linear conditional variance.
Sarem Seitz
When Point Forecasts Are Completely Useless
While point forecasts are very popular, be aware of some unlucky pitfalls.
Why I prefer Probabilistic Forecasts - Hitting Time Probabilities
Point forecasts are good for making decisions. With probabilistic forecasts, you can also make the right ones.
Random Forests and Boosting for ARCH-like volatility forecasts
Tree models are not just useful for point and mean forecasts.
Forecasting with Decision Trees and Random Forests
Random Forests are flexible and powerful when it comes to tabular data. Do they also work for time-series forecasting? Let's find out.
Multivariate GARCH with Python and Tensorflow
One primary limitation of GARCH is the restriction to a single dimensional time-series. In reality, however, we are typically dealing with multiple time-series.
Cointegrated time-series and when differencing might be bad
You have heard about integrated time-series data but what about cointegration?