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Deep Learning-Based Option Pricing Under the SVSI Model: Incorporating Stochastic Volatility and Interest Rates | ||
| Journal of Mathematics and Modeling in Finance | ||
| مقالات آماده انتشار، پذیرفته شده، انتشار آنلاین از تاریخ 23 تیر 1405 اصل مقاله (4 M) | ||
| نوع مقاله: Research Article | ||
| شناسه دیجیتال (DOI): 10.22054/jmmf.2026.92053.1273 | ||
| نویسندگان | ||
| Seyed Nourollah Mousavi* ؛ Ali Bolfakeh؛ Sima Mashayekhi؛ Rasoul Beykverdi | ||
| Department of Mathematics, Faculty of Science, Arak University, Arak 38156-8-8349, Iran. | ||
| چکیده | ||
| The SVSI model is an advanced financial framework that simultaneously accounts for stochastic volatility and stochastic interest rates in the pricing of financial instruments such as options. The primary objective of this model is to enhance forecasting accuracy and risk assessment. In this study, an optimized deep learning model is proposed for option pricing under the SVSI framework, considering both dependence and independence between the underlying asset and the interest rate. The Monte Carlo method, combined with the conditional Monte Carlo variance reduction technique, is employed to generate optimal pricing data for options under the SVSI model. The performance of the proposed deep learning model is evaluated by comparing it with the generated data. Additionally, Bitcoin option pricing is examined using the proposed model. | ||
| کلیدواژهها | ||
| Option pricing؛ SVSI model؛ Conditional Monte Carlo؛ Deep learning؛ Bitcoin option data | ||
| مراجع | ||
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