  The pricing of financial derivatives, which requires massive calculations and
close-to-real-time operations under many trading and arbitrage scenarios, were
largely infeasible in the past. However, with the advancement of modern
computing, the efficiency has substantially improved. In this work, we propose
and design a multi-path option pricing approach via autoregression (AR) process
and Monte Carlo Simulations (MCS). Our approach learns and incorporates the
price characteristics into AR process, and re-generates the price paths for
options. We apply our approach to price weekly options underlying Taiwan Stock
Exchange Capitalization Weighted Stock Index (TAIEX) and compare the results
with prior practiced models, e.g., Black-Scholes-Merton and Binomial Tree. The
results show that our approach is comparable with prior practiced models.
