I am building a trading strategy that predicts the current period returns using historical returns (think e.g. using an estimated OLS model to predict next weeks return based on this weeks return). However, I am at loss in picking the window I should use for estimating the model.
The way I see it, there are two ways I can do this: a) pick a fixed window length - e.g. 1 year (52 weekly observations), and re-estimate the model every week. However, depending on the asset the slope of the regression tends to change, and is especially suspect to few 'outlier' cases, which makes me question whether the model is still theoretically sound. b) use all available data, and roll to window forward every week, re-estimating the model. However, if the relationship is time variant, I think this approach will lead to extended periods of negative returns if there is a break in the model that a shorter window might capture better.
How should I go about determining which method to use? I could of course back test different filtering strategies based on these two, but the more complicated, the more risk of overfitting IMO, and hence I'd prefer a more simplistic but statistically sound method for determining which way to go.
Any suggestions? Would also love to read good papers that deal with this issue if any come to mind.