The optimal model of artificial neural networks for predicting the option price under the asset model SVSI

Subject Areas :

Amir Mohammadzadeh ¹ , fatemeh fakouri liavoli ² , Ali Bolfake ³

1 - Assistant Professor of financial management, ,Department of Financial Management, Qazvin Branch, Islamic Azad University, Qazvin, Iran
2 - Financial. Faculty of Management and Accounting. Islamic Azad University of Qazvin. Qazvin. Iran
3 - Department of Mathematics, Faculty of Sciences, Arak University, Arak 38156-8-8349, Iran

Received: 2024-03-19 Accepted : 2024-08-12

Keywords: Deep learning, option pricing, stochastic volatility, stochastic interest rates, Monte Carlo simulation,

Abstract :

The present study examines the application of deep learning in forecasting option pricing under the stochastic interest rate and stochastic volatility model. In the stochastic interest rate model, the interest rate of financial products is randomly determined at different times. This randomization causes volatility in prices and interest rates. In this research, characteristic function, Monte Carlo simulation and conditional Monte Carlo simulation are used to generate data. Monte Carlo simulation is used as a random and probabilistic method to obtain statistical results and predictions related to option pricing by generating data that belong to the characteristic function. since the financial markets contain a lot of data, in order to be able to manage this data as a financial expert and make decisions based on it, one must master the science of deep learning. The tools available in deep learning and finance affairs are also used to determine and predict the option price under the stochastic volatility model and stochastic interest rate model. Results show that the efficiency of deep learning models, in terms of time and accuracy, compared to traditional methods such as Monte Carlo method is more. The results of this research can be used as a practical tool for investment decisions and risk management.

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