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We propose a new, data-driven approach for efficient pricing of - fixed- andfloat-strike - discrete arithmetic Asian and Lookback options when theunderlying process is driven by the Heston model dynamics. The method proposedin this article constitutes an extension of our previous work, where theproblem of sampling from time-integrated stochastic bridges was addressed. Themodel relies on the Seven-League scheme, where artificial neural networks areemployed to "learn" the distribution of the random variable of interestutilizing stochastic collocation points. The method results in a robustprocedure for Monte Carlo pricing. Furthermore, semi-analytic formulae foroption pricing are provided in a simplified, yet general, framework. The modelguarantees high accuracy and a reduction of the computational time up tothousands of times compared to classical Monte Carlo pricing schemes.

On Pricing of Discrete Asian and Lookback Options under the Heston Model