Article

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

Sara Mehrab DanialiGraduate School of Service and Trade, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, RussiaSergey BarykinGraduate School of Service and Trade, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, RussiaIrina KapustinaGraduate School of Service and Trade, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, RussiaFarzin Mohammadbeigi KhortabiInstitute of Industrial Management, State University of Management, 109542 Moscow, RussiaSergey Mikhailovich SergeevGraduate School of Industrial Management, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, RussiaOlga KalininaGraduate School of Industrial Management, Peter the Great St. Petersburg Polytechnic University, 195251 St. Petersburg, RussiaAlexey MikhaylovDepartment of Banking and Financial Markets, Financial University under the Government of the Russian Federation, 124167 Moscow, RussiaRoman VeynbergComputer Science Department, Plekhanov Russian University of Economics, 117997 Moscow, RussiaLiubov V. ZasovaDepartment of Economics and Management, Sechenov University, 119991 Moscow, RussiaTomonobu SenjyuDepartment of Electrical and Electronics Engineering, University of the Ryukyus, Okinawa 903-0213, Japan

2021en

ABI

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.

Identifiers

DOI: 10.3390/su132414011

Citations and references

Cited by 30 references

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