Predicting Futures Pricing Based on VIX Volatility Index Using Machine Learning in the Iranian Capital Market
Subject Areas : Financial engineeringSimin Rajizade 1 , Sepideh Rajizadeh 2
1 - Department of Accounting, Faculty of Management and Accounting, Payam Noor University, Tehran, Iran
2 - Department of Accounting, National University of Skills, Tehran, Iran
Keywords: : Futures Pricing, VIX Volatility Index, Machine Learning Methods,
Abstract :
Today, in the world's reputable financial markets, risk management tools, especially financial derivatives, including futures and options, are of great importance. If the pricing of options is estimated correctly, by minimizing the risk involved, more investors will be willing to invest in options.The present study shows that integrating the VIX index with machine learning methods, especially in volatile environments such as the Iranian market, can serve as a powerful analytical tool for predicting futures pricing and risk management. The main goal is to reduce forecast error and improve accuracy in risk management by using advanced machine learning models. Machine learning, using advanced algorithms, can identify complex patterns in historical market data and use them to predict futures pricing. As a result, predicting futures pricing based on the VIX index and using machine learning is an efficient and innovative method for improving the accuracy of forecasts and risk management in the Iranian capital market. The data used includes daily gold coin option trading information on the Iran Commodity Exchange during the period 1392 to 1402. The data are divided into three periods: pilot (2014-2019), validation (2020), and testing (2021-2024). The results show that machine learning models, especially the MLP model with an error of 28%, perform best in predicting futures prices. The use of these methods significantly reduces measurement error and provides an efficient tool for decision-making under conditions of market uncertainty. Also, the VIX index was confirmed as a key measure for assessing market volatility. The development of hybrid models and the use of high-frequency data can help further improve the accuracy of forecasts in the future.
راجی زاده، سیمین. (1401). ارزیابی شاخص نوسان VIX در بازار سرمایه ایران و تأثیر قیمتگذاری آتی آن با استفاده از مدل گارو. مهندسی مالی و مدیریت اوراق بهادار، 52(3): 60-80.
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