| Issue |
E3S Web of Conf.
Volume 402, 2023
International Scientific Siberian Transport Forum - TransSiberia 2023
|
|
|---|---|---|
| Article Number | 03022 | |
| Number of page(s) | 6 | |
| Section | Mathematical Modeling, IT, Industrial IoT, AI, and ML | |
| DOI | https://doi.org/10.1051/e3sconf/202340203022 | |
| Published online | 19 July 2023 | |
Ensembling two deep learning algorithms to efficiently solve the problem of predicting volatility in applied finance
T.F. Gorbachev Kuzbass State Technical University, 650000, Kemerovo, 28 Vesennya st., Russian Federation
* Corresponding author: pylovpa@kuzstu.ru
Volatility is one of the most commonly used terms in the trading platform. In financial markets, volatility reflects the magnitude of price fluctuations. High volatility is associated with periods of market turbulence and sharp price fluctuations, while low volatility characterizes more relaxed pricing. When trading options, it is especially important for trading firms to accurately predict volatility values, since the price of options is directly related to the profit of a trading firm. A proactive artificial intelligence model that allows predicting volatility for future periods of time will be presented in this article.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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