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

Sara Mehrab Daniali, Sergey Evgenievich Barykin, Irina Vasilievna Kapustina, Farzin Mohammadbeigi Khortabi, Sergey Mikhailovich Sergeev, Olga Vladimirovna Kalinina, Alexey Mikhaylov, Roman Veynberg, Liubov Zasova and Tomonobu Senjyu
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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
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

Sustainability, 2021, vol. 13, issue 24, 1-14

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.

Keywords: sustainable financial markets; volatility index; deep neural network; convolution of probability (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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