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Predicting Volatility Index According to Technical Index and Economic Indicators on the Basis of Deep Learning Algorithm

Author

Listed:
  • Sara Mehrab Daniali

    (Graduate School of Service and Trade, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia)

  • Sergey Evgenievich Barykin

    (Graduate School of Service and Trade, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia)

  • Irina Vasilievna Kapustina

    (Graduate School of Service and Trade, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia)

  • Farzin Mohammadbeigi Khortabi

    (Institute of Industrial Management, State University of Management, 109542 Moscow, Russia)

  • Sergey Mikhailovich Sergeev

    (Graduate School of Industrial Management, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia)

  • Olga Vladimirovna Kalinina

    (Graduate School of Industrial Management, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, Russia)

  • Alexey Mikhaylov

    (Department of Banking and Financial Markets, Financial University under the Government of the Russian Federation, 124167 Moscow, Russia)

  • Roman Veynberg

    (Computer Science Department, Plekhanov Russian University of Economics, 117997 Moscow, Russia)

  • Liubov Zasova

    (Department of Economics and Management, Sechenov University, 119991 Moscow, Russia)

  • Tomonobu Senjyu

    (Department of Electrical and Electronics Engineering, University of the Ryukyus, Okinawa 903-0213, Japan)

Abstract

The Volatility Index (VIX) is a real-time index that has been used as the first measure to quantify market expectations for volatility, which affects the financial market as a main actor of the overall economy that is sensitive to the environmental and social aspects of investors and companies. The VIX is calculated using option prices for the S&P 500 Index (SPX) and is expressed as a percentage. Taking into account that VIX only shows the implicit volatility of the S&P 500 for the next 30 days, the authors develop a model for a near-optimal state trying to avoid uncertainty and insufficient accuracy. The researchers are trying to make a contribution to the theory of socially responsible portfolio management. The developed approach allows potential investments to make decisions regarding such important topics as ethical investing, performance analysis, as well as sustainable investment strategies. The approach of this research allows to use deep probabilistic convolutional neural networks based on conditional variance as a linear function of errors with the aim of estimating and predicting the VIX. For this purpose, the use of technical indicators and economic indexes such as Chicago Board Options Exchange (CBOE) VIX and S&P 500 is considered. The results of estimating and predicting the VIX with the proposed method indicate high precision and create a certainty in modeling to achieve the goals.

Suggested Citation

  • Sara Mehrab Daniali & Sergey Evgenievich Barykin & Irina Vasilievna Kapustina & Farzin Mohammadbeigi Khortabi & Sergey Mikhailovich Sergeev & Olga Vladimirovna Kalinina & Alexey Mikhaylov & Roman Veyn, 2021. "Predicting Volatility Index According to Technical Index and Economic Indicators on the Basis of Deep Learning Algorithm," Sustainability, MDPI, vol. 13(24), pages 1-14, December.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:24:p:14011-:d:705966
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    References listed on IDEAS

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    1. Hyun Sik Sim & Hae In Kim & Jae Joon Ahn, 2019. "Is Deep Learning for Image Recognition Applicable to Stock Market Prediction?," Complexity, Hindawi, vol. 2019, pages 1-10, February.
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    Cited by:

    1. Pavel Baboshkin & Alexey Mikhaylov & Zaffar Ahmed Shaikh, 2022. "Sustainable Cryptocurrency Growth Impossible? Impact of Network Power Demand on Bitcoin Price," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 3, pages 116-130, June.
    2. Hsu, Ching-Chi & Chau, Ka Yin & Chien, FengSheng, 2023. "Natural resource volatility and financial development during Covid-19: Implications for economic recovery," Resources Policy, Elsevier, vol. 81(C).

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