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The value of Monte Carlo model-based variance reduction technology in the pricing of financial derivatives

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  • Yunyu Zhang

Abstract

The purpose of the study is to reduce the error in the pricing process of financial derivatives, as well as to obtain more accurate product values, thereby reducing transaction costs, accelerating transaction speed, establishing a larger investment scale, and enabling investors to obtain excellent returns under market conditions as much as possible. Based on the variance reduction technology, a Monte Carlo model that can effectively analyze financial prices is added to analyze price fluctuations and find the optimal holding time for users of financial derivatives, thereby reducing the risk of holding the financial derivatives. The results show that the Monte Carlo model-based variance reduction technology can significantly improve the simulation efficiency of financial derivatives pricing. In addition, the importance sampling method is used to optimize the selection, thereby making it closer to the theoretical values. The proposed method is easy to implement and has higher computational efficiency, which can ensure the financial benefits of users holding financial derivatives during the holding period. It can be seen that the Monte Carlo model-based variance reduction technology has high application value in the pricing of financial derivatives, and it is of great significance for the pricing of other products.

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  • Yunyu Zhang, 2020. "The value of Monte Carlo model-based variance reduction technology in the pricing of financial derivatives," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-13, February.
    • Handle: RePEc:plo:pone00:0229737
    DOI: 10.1371/journal.pone.0229737
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    References listed on IDEAS

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    Cited by:

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    3. Agni Rakshit & Gautam Bandyopadhyay & Tanujit Chakraborty, 2024. "Hedge Error Analysis In Black Scholes Option Pricing Model: An Asymptotic Approach Towards Finite Difference," Papers 2405.02919, arXiv.org.
    4. Jean C. Kouam & Simplice A. Asongu & Bin J. Meh & Robert Nantchouang & Fri L. Asanga & Denis A. Foretia, 2024. "A Synthetic Indicator of the Quality of Support for Businesses in Burkina-Faso, Cameroon, and Ghana," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(1), pages 4939-4974, March.
    5. Andrea Senova & Alica Tobisova & Robert Rozenberg, 2023. "New Approaches to Project Risk Assessment Utilizing the Monte Carlo Method," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    6. Jaydip Sen & Rajdeep Sen & Abhishek Dutta, 2021. "Machine Learning in Finance-Emerging Trends and Challenges," Papers 2110.11999, arXiv.org.

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