I have successfully fit an ARIMA model to a time series of the daily returns of power futures prices. The question I have is: How can I create a prediction interval for the prices? Or, alternatively, is there a nonparametric alternative to ARIMA that you would recommend? I understand econometric models are a bit iffy for long-term forecasts, and I would like this model to be extendable to at least six months (~120 periods).
Simply taking the cumulative product of the forecasted returns and the most recent historical price creates a band that diverges far too quickly to be realistic (sample image below for a 20-day futures price forecast using an ARIMA(2, 0, 2) model on the March 2021 contract as of December 2020). If it makes a difference, to make the data stationary and reduce the required "d" order, please note that the data were a) first standardized and b) then transformed via Yeo-Johnson before they were fed into the ARIMA model. Needless to say, I then reversed the transformations to scale the data. The ARIMA parameters were calculated using auto_arima in Python.