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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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    1. G. Rigatos & N. Zervos, 2017. "Detection of Mispricing in the Black–Scholes PDE Using the Derivative-Free Nonlinear Kalman Filter," Computational Economics, Springer;Society for Computational Economics, vol. 50(1), pages 1-20, June.
    2. Mario Hefter & Arnulf Jentzen, 2019. "On arbitrarily slow convergence rates for strong numerical approximations of Cox–Ingersoll–Ross processes and squared Bessel processes," Finance and Stochastics, Springer, vol. 23(1), pages 139-172, January.
    3. Roy, Rudra Prosad & Sinha Roy, Saikat, 2017. "Financial contagion and volatility spillover: An exploration into Indian commodity derivative market," Economic Modelling, Elsevier, vol. 67(C), pages 368-380.
    4. Xuan Shen & Valentina Hartarska, 2018. "Winners and losers from financial derivatives use: evidence from community banks," Applied Economics, Taylor & Francis Journals, vol. 50(41), pages 4402-4417, September.
    5. Hoberg, Gerard & Moon, S. Katie, 2017. "Offshore activities and financial vs operational hedging," Journal of Financial Economics, Elsevier, vol. 125(2), pages 217-244.
    6. Jan De Spiegeleer & Dilip B. Madan & Sofie Reyners & Wim Schoutens, 2018. "Machine learning for quantitative finance: fast derivative pricing, hedging and fitting," Quantitative Finance, Taylor & Francis Journals, vol. 18(10), pages 1635-1643, October.
    7. Huisu Jang & Jaewook Lee, 2019. "Generative Bayesian neural network model for risk-neutral pricing of American index options," Quantitative Finance, Taylor & Francis Journals, vol. 19(4), pages 587-603, April.
    8. Jr‐Wei Huang & Sharon S. Yang & Chuang‐Chang Chang, 2018. "Modeling temperature behaviors: Application to weather derivative valuation," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 38(9), pages 1152-1175, September.
    9. Kim, Huong Trang & Papanastassiou, Marina & Nguyen, Quang, 2017. "Multinationals and the impact of corruption on financial derivatives use and firm value: Evidence from East Asia," Journal of Multinational Financial Management, Elsevier, vol. 39(C), pages 39-59.
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    Cited by:

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    2. Alica Tobisova & Andrea Senova & Robert Rozenberg, 2022. "Model for Sustainable Financial Planning and Investment Financing Using Monte Carlo Method," Sustainability, MDPI, vol. 14(14), pages 1-18, July.
    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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