The Option Pricing of Monte Carlo Simulation: with the Empirical  Research in the China ETF 50 Market

This paper provides a QMC method to solve high-dimensionality options. Paper provided comprehensive theories and evident showed that the QMC method with variance reduction technology has high accuracy in solving higher dimensionality options issues, also, in application with SSE 50ETF options, we generated same results. The option pricing problem always as a core topic of quantitative finance. For simple option pricing, the Black Scholes (BS) model is normally used . But for complex options, the BS model may not work well on path Dependence option. Therefore, the emergence of MC provides another method to solve option pricing. However, the MC also has some inherent limitations, such as large errors in estimation and lower calculation speed, etc. Currently development of statistics and computer technology, scholars began to use quasi-random number (QRN) improve to MC methods, QMC used Low-discrepancy sequences (QRN) to improving the efficiency and accuracy of stochastic simulation，thereby increase accuracy and efficiency of the option pricing.