Pricing the American put options requires solving an optimal stopping problem and therefore is a challenge for the setting up of simulation parameters.This paper uses least square Monte Carlo（LSMC） simulation to pri...Pricing the American put options requires solving an optimal stopping problem and therefore is a challenge for the setting up of simulation parameters.This paper uses least square Monte Carlo（LSMC） simulation to price the American put options and output the optimal simulation steps and number of Hermite basis functions.The results suggest：with different time cost and error tolerance,investors can choose the optimal simulation steps and number of basis function individually to price American put options numerically.Generally,with the pre-limitation in the section ＂least square Monte Carlo simulation＂,a number of basis equals 4,15 000 simulation steps for Hermite basis function appear to be sufficient for the method.展开更多
文摘Pricing the American put options requires solving an optimal stopping problem and therefore is a challenge for the setting up of simulation parameters.This paper uses least square Monte Carlo（LSMC） simulation to price the American put options and output the optimal simulation steps and number of Hermite basis functions.The results suggest：with different time cost and error tolerance,investors can choose the optimal simulation steps and number of basis function individually to price American put options numerically.Generally,with the pre-limitation in the section ＂least square Monte Carlo simulation＂,a number of basis equals 4,15 000 simulation steps for Hermite basis function appear to be sufficient for the method.
