I am running ridge regression on time series data for the purposes of prediction. The data is non-normal, highly correlated and prone to fat tails either way (financial data). I am not removing the data because or errors, just because it helps prediction.
I currently standardise and remove anything above $|x|$ = 3, 4, or 5 standard deviations, to improve prediction based on MAE, MSE and adjusted $R$2. However I was wondering if this is a good approach or if clipping or perhaps winsorizing data is generally a preferred method in these cases. Are there a good arguments to be made for any of the method over the others?