Pricing of Arithmetic Average Asian Option by Combining Variance Reduction and Quasi-Monte Carlo Method

Financial derivatives have developed rapidly over the past few decades due to their risk-averse nature, with options being preferred financial flexible contractual mechanisms, particularly Asian options. The Black–Scholes stock option pricing model is often used in conjunction Monte Carlo simulations for pricing. However, assumes that volatility of asset returns constant, which does not square practical markets. Additionally, simulation suffers from slow error convergence. To address these issues, we first correct using a GARCH model. Then, low convergence rate method improved variance reduction techniques. Meanwhile, quasi-Monte approach based on discrepancy sequences refine rate. We also provide experiment and result analysis validate effectiveness our proposed method.