Autors: Georgiev S., Todorov V., Georgiev I., Idirizov B., Traneva V., Tranev S., Sapundzhi F., Lazarova, M. D., Todorov M.
Title: Intelligent Stochastic Approaches for Valuating European Options
Keywords: Computational finance, Intelligent decision making, Monte Carlo, Option pricing, Uncertainty

Abstract: The pricing of multi-dimensional options presents significant challenges, as it is a fundamental issue in modern large-scale financial analysis. A European call option grants its holder the right, but not the obligation, to acquire a specified quantity of an underlying asset S at a predetermined strike price K on a fixed maturity date T. In financial modeling under uncertainty, Monte Carlo and quasi-Monte Carlo techniques have emerged as powerful tools for tackling complex valuation problems. This paper investigates the intelligent optimization of simulation-based approaches to accurately determine the fair value of multi-dimensional European options. Monte Carlo methods, particularly in higher-dimensional settings, are well-suited for such pricing tasks due to their flexibility and effectiveness. In this study, we introduce intelligent optimization strategies that leverage low-discrepancy sequences and variance reduction techniques, significantly enhancing the precision of conventional Monte Carlo simulations. These refinements lead to more reliable pricing outcomes, which are critical for informed financial decision-making under uncertainty. Furthermore, the proposed methodology demonstrates robust performance in scenarios where traditional deterministic approaches struggle, such as high-dimensional spaces, intricate contract structures, and dynamically evolving market conditions. By integrating intelligent algorithms into Monte Carlo simulations, this approach contributes to the broader domain of computational finance and decision-making in uncertain environments.

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Issue

Lecture Notes in Networks and Systems, vol. 1530 LNNS, pp. 291-300, 2025, Switzerland, https://doi.org/10.1007/978-3-031-98565-2_32

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