As Quants, we soon learn to optimise models, by fitting them to historical time series, e.g. the historical daily returns of some stock.
But the historical series of daily returns is just one realisation, out of many possibile series that could originate from the same distribution of daily returns.
So if I fit my model to a specific realisation of daily returns -which happens to be the historical series- I might be overfitting the model.
Wouldn't it be more correct to optimise the model, based on N boostrapped series of daily returns, all originating from the actual historical series?
Where would this technique lie, on the spectrum from stupid to industry standard? 😋