Document Type : Research Article
Authors
Department of Mathematics, Faculty of Science, Arak University, Arak 38156-8-8349, Iran.
Abstract
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.
Keywords
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