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I understand how to use the longstaff schwartz method in Monte Carlo to compute the continuation value of path dependent options but someone recently mentioned another technique called "Hindsight overhedge". I can't find any reference to it. Has anyone come across Hindsight overhedge in Monte Carlo simulation and can point reference to it?

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I believe this may be referring to a procedure whereby one uses the ‘future’ Monte Carlo paths to determine optimal exercise. For example, consider an exercise decision at $T_1$ within a path dependent option that expires at $T_2>T_1$. Then to determine whether to exercise at $T_1$, examine each path in [$T_1,T_2$] to decide if continuation value > exercise value, and exercise ‘retrospectively’ accordingly. This presumably gives an upper bound on the value, since you are cheating by looking into the future.

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  • $\begingroup$ I have also heard it described as "perfect knowledge" montecarlo. $\endgroup$ – will Jul 20 at 1:12
  • $\begingroup$ Thanks @dm63. If I understand correctly the key difference between Longstaff Schwartz and Hindsight is that in Hindsight approach the continuation value is worked out per path whereas in Longstaff regression is done on all (in the money) paths. Is there any published material going into further details on this? $\endgroup$ – quantlearner Jul 22 at 16:14
  • $\begingroup$ i think you're right - but i cant locate any published materials $\endgroup$ – dm63 Jul 23 at 12:25

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