2022/11/07 by Leonardo Perotti, Perotti, Leonardo, Lech A. Grzelak +1
Economics, Econometrics and Finance · #Computational Finance (q-fin.CP) #FOS: Economics and business #Stochastic processes and financial applications
paper · pdf · doi:10.48550/arxiv.2211.03638
openalex publication_date 2022/11/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
We propose a new, data-driven approach for efficient pricing of - fixed- and float-strike - discrete arithmetic Asian and Lookback options when the underlying process is driven by the Heston model dynamics. The method proposed in this article constitutes an extension of our previous work, where the problem of sampling from time-integrated stochastic bridges was addressed. The model relies on the Seven-League scheme, where artificial neural networks are employed to "learn" the distribution of the random variable of interest utilizing stochastic collocation points. The method results in a robust procedure for Monte Carlo pricing. Furthermore, semi-analytic formulae for option pricing are provided in a simplified, yet general, framework. The model guarantees high accuracy and a reduction of the computational time up to thousands of times compared to classical Monte Carlo pricing schemes.