ARIMA and GARCH are old news for predicting volatility time series of asset returns. I am aware of papers that replace ARIMA and GARCH with machine learning algorithms to predict financial volatility more accurately, and so this question is a reference request for a survey of what's out there:
How do the performances of the following, and other, machine learning algorithms compare with one another, and GARCH, at forecasting volatility for horizons longer than just 1-day/step ahead?
- random forest
- support vector regression (SVR)
- gradient boosting
- K nearest neighbors, etc)
And are the machine learners also implemented in an autoregressive formula like GARCH (i.e. they use preceding historical volatility observations to estimate the current volatility)?
Also what do the research articles say regarding the theoretical reason for applying machine learning to volatility time series in particular?