3

1) Do we need to deal with infinite dimensional spaces? Yes, I think you need an infinite dimensional pay-off space. Your remark that a finite sample spans a finite dimensional space of pay-offs is true. But you would like to prove convergence of the method for any pay-off, i.e. for all possible samples of all sizes. 2) In the case that we want to insist ...


2

Recently, I co-authored a paper (Arxiv | SSRN) on the issue. You can efficiently remove the look-ahead bias (a.k.a., foresight bias) using leave-out-out-cross-validation (LOOCV) method. Basically, the procedure is (i) take out one sample ,(ii) run regression using the rest, (iii) get the prediction on the removed sample, and (iv) repeat the process for ...


2

This study seems to be on point: http://christian-fries.de/finmath/foresightbias/Fries_ForesightBias.pdf


1

As Alex C says in the comments, Longstaff and Schwarz did consider multiple factors and mention it as one of the advantages (page 114 in the journal): By its nature, simulation is a promising alternative to traditional finite difference and binomial techniques and has many advantages as a framework for valuing, risk managing, and optimally exercising ...


Only top voted, non community-wiki answers of a minimum length are eligible