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22 November 2022 Monte Carlo algorithm for pricing options under stochastic volatility models
Caijuan Kang M.D., Hongying Wu M.D.
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Proceedings Volume 12475, Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022); 124751D (2022) https://doi.org/10.1117/12.2659363
Event: Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022), 2022, Hulun Buir, China
Abstract
Monte Carlo (MC) simulation is a powerful technic to valuate option, but few literature discuss option pricing under stochastic volatility model (SVM). In this paper, we apply MC method to price European options under SVMs. Firstly, given correlative coefficient, a formula generating norm distribution random variables is established. Then, MC scheme is proposed for pricing European options under Heston, Hull-White and Hyper-geometric models. Numerical experiments illustrate the efficiency and accuracy of MC algorithm. With large number of simulated paths and time partition, MC solutions become stable. The proposed MC method can be extended to general option pricing, such as local stochastic volatility models and so on.
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Caijuan Kang M.D. and Hongying Wu M.D. "Monte Carlo algorithm for pricing options under stochastic volatility models", Proc. SPIE 12475, Second International Conference on Advanced Algorithms and Signal Image Processing (AASIP 2022), 124751D (22 November 2022); https://doi.org/10.1117/12.2659363
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KEYWORDS
Monte Carlo methods
Stochastic processes
Computer simulations
Mathematical modeling
Numerical analysis
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