  This paper explores alternative regression techniques in pricing American put
options and compares to the least-squares method (LSM) in Monte Carlo
implemented by Longstaff-Schwartz, 2001 which uses least squares to estimate
the conditional expected payoff to the option holder from continuation. The
pricing is done under general model framework of Bakshi, Cao and Chen 1997
which incorporates, stochastic volatility, stochastic interest rate and jumps.
Alternative regression techniques used are Artificial Neural Network (ANN) and
Gradient Boosted Machine (GBM) Trees. Model calibration is done on American put
options on SPY using these three techniques and results are compared on out of
sample data.
