Using the TIMEMIXER approach for volatility forecasting

An application of time mixing for volatility forecasting can be an important advancement for risk management. Research has extended the work on GARCH to an extreme, but there may be other techniques in time series forecasting that can be applied to financial time series that may be very useful. A recent paper focused on TimeMixers which employs different time scales as a method to improve forecasts. See "Volatility Forecasting in Global Financial Markets Using TimeMixer".

The idea behind TimeMixers is straight-forward. There is imbedded in any times series relationship with different timeframes that can exploited. There can be long-term seasonality. There can be cycles or trends that are longer than a few days that will not be captured with daily data. Classic time series in ARMA models can handle seasonality and can identify autocorrelation at different lengths, but a more explicit breakdown of data may improve forecasts. 

I like the technique used and the author applied it to a broad set of markets, but I was disappointed that there was no testing against other types of models for volatility. This process looks interesting but it is not clear it is any better than what we already have. The MAE, MSE, and RMSE all are low especially for short-term forecasts, but the quality of technique must be balanced with the results, the ease of understanding, and the ease of implementation. This paper does not make that strong relative case for TimeMixer ML.