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The Longstaff-Schwartz LSM approach is nowadays ubiquitous(at least in the academic literature) in pricing path dependant derivatives. Up to now I have mostly worked with lattice methods. My experience in impelemting those has shown that there are often ways to tweak them and also lot of pittfalls along the way.

To those of you who have some experience in working with LSM:

  1. Aside from the usual Monte-Carlo-Optimization techniques (e.g. variance reduction, importance sampling etc.) are there any optimizations that are particular to the LSM approach ? (Perhaps some paper on the choice of the interpolating polynomial) ?

  2. What are possible pittfalls when implementing and working with the model ? When can LSM go really wrong/ in which cases does it fail to price correctly ?

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Upvoted, though I'm not sure "ubiquitous" is really true. What desks is it ubiquitous for? Or are you thinking more of academic literature? –  Brian B Mar 4 at 16:54
well in academic literature for sure. Also I am not aware of any other methods but lattice (and these don't work in n-dimensions) - I am not working in the front office so I wouldn't know about which desks use what - I will edit the question. –  Probilitator Mar 4 at 16:59

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