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

American Put Option pricing using Least squares Monte Carlo method under  Bakshi, Cao and Chen Model Framework (1997) and comparison to alternative  regression techniques in Monte Carlo