## Monday, April 22, 2013

### Quasi Monte-Carlo & Longstaff-Schwartz American Option price

In the book Monte Carlo Methods in Financial Engineering, Glasserman explains that if one reuses the paths used in the optimization procedure for the parameters of the exercise boundary (in this case the result of the regression in Longstaff-Schwartz method) to compute the Monte-Carlo mean value, we will introduce a bias: the estimate will be biased high because it will include knowledge about future paths.

However Longstaff and Schwartz seem to just reuse the paths in their paper, and Glasserman himself, when presenting Longstaff-Schwartz method later in the book just use the same paths for the regression and to compute the Monte-Carlo mean value.

How large is this bias? What is the correct methodology?

I have tried with Sobol quasi random numbers to evaluate that bias on a simple Bermudan put option of maturity 180 days, exercisable at 30 days, 60 days, 120 days and 180 days using a Black Scholes volatility of 20% and a dividend yield of 6%. As a reference I use a finite difference solver based on TR-BDF2.

I found it particularly difficult to evaluate it: should we use the same number of paths for the 2 methods or should we use the same number of paths for the monte carlo mean computation only? Should we use the same number of paths for regression and for monte carlo mean computation or should the monte carlo mean computation use much more paths?

I have tried those combinations and was able to clearly see the bias only in one case: a large number of paths for the Monte-Carlo mean computation compared to the number of paths used for the regression using a fixed total number of paths of 256*1024+1, and 32*1024+1 paths for the regression.

FDM price=2.83858387194312
Longstaff discarded paths price=2.8385854695510426
Longstaff reused paths price=2.8386108892756847

Those numbers are too good to be a real. If one reduces too much the total number of paths or the number of paths for the regression, the result is not precise enough to see the bias. For example, using 4K paths for the regression leads to 2.83770 vs 2.83767. Using 4K paths for regression and only 16K paths in total leads to 2.8383 vs 2.8387. Using 32K paths for regressions and increasing to 1M paths in total leads to 2.838539 vs 2.838546.

For this example the Longstaff-Schwartz price is biased low, the slight increase due to path reuse is not very visible and most of the time does not deteriorate the overall accuracy. But as a result of reusing the paths, the Longstaff-Schwartz price might be higher than the real value.