# Importance Sampling for Least Square Monte Carlo [duplicate]

I am currently trying to implement and model an Importance Sampling estimator for Longstaff and Schwartz algorithm for pricing American put options. It is used such that more paths are in-the-money such that less simulation is required and also the variance is reduced.

I am following the steps by Moreni and simulate $n$-paths of a drifted geometric brownian motion given by:

$dS_t = S_t\big[(r+\theta \sigma)dt+\sigma dW_t\big]$.

Then moreni defines the likelihood ratio $L_\tau^\theta=exp\{-\theta W_t+0.5 \theta^2 t \}$.

By the stopping time theorem, we want to find for each path $n$ $\sup_\tau \mathbb{E}[f^{(n)}(\tau,S_\tau)]$ the corresponding Importance sampling estimator $\sup_\tau \mathbb{E}[L_\tau^\theta f^{(n)}(\tau,S_\tau)]$, where $f$ is the payoff function.

When we have found the stopping time for each path $n$ we can take the average and get the desired put price at time 0.

My question is that is the wiener process in the likelihood ratio the random number at $\textit{optimal}$ time $\tau$ for path some path $n_i$? And at the same time should $t$ be the optimal time $\tau$ for path $n_i$?

Since in R the standard L&S algorithm is existent in the package library(LSMonteCarlo), i modified the function AmerPutLSM, such that it simulate with drift $\theta$ and i set the interest rate to 0. However, I seem to get very high prices when I try to simulate compared to the standard one. So I am not sure if I really have understood the math correctly. I use negative values for $\theta$ and it should be around -0.5 and -1 according to Moreni. But the prices I get are really deviating.

Here is the code, that I modified:

firstValueRow <- function (x)
{
cumSumMat <- matrix(NA, nrow = dim(x), ncol = dim(x))
for (i in 1:(dim(x))) {
cumSumMat[i, ] <- cumsum(x[i, ])
}
cumSumMat2 <- cbind(matrix(0, nrow = dim(x), ncol = 1), cumSumMat[, -(dim(x))])
ResultMat <- matrix(NA, nrow = dim(x), ncol = dim(x))
for (i in 1:dim(x)) {
ResultMat[, i] <- ifelse(cumSumMat2[, i] > 0, 0, x[,i])
}
return(ResultMat)
}

Spot = 36
sigma = 0.8
theta = -1
mu = 0
n = 1000
m = 50
Strike = 40
r = 0
dr = 0
mT = 1

dt <- mT/m
GBM <- matrix(NA, nrow = n, ncol = m)
Zlist <- matrix(NA, nrow = n, ncol = m)
for (i in 1:n)
{
Z <- rnorm(m, mean = mu, sd = 1)
GBM[i, ] <- Spot * exp(cumsum(((r+theta*sigma-0.5*sigma*sigma)*dt)+(sigma*(sqrt(dt))*Z)))
Zlist[i, ] <- Z
}
X <- ifelse(GBM < Strike, GBM, NA) #stock rates only in the money
### payoff importance sampling
Ltheta <- exp(-theta*Zlist-0.5*theta*theta*dt)
CFL <- matrix(Ltheta*pmax(0, Strike - GBM), nrow = n, ncol = m) #cashflows

Xsh <- X[, -m]
X2sh <- Xsh * Xsh

Y1 <- CFL * exp(-1 * r * dt)
Y2 <- cbind((matrix(NA, nrow = n, ncol = m - 1)), Y1[, m]) #value of derivate at time t+1

CV <- matrix(NA, nrow = n, ncol = m - 1) #continuation value
for (i in (m - 1):1)
{
reg1 <- lm(Y2[, i + 1] ~ Xsh[, i] + X2sh[, i])