Ensembling two deep learning algorithms to efficiently solve the problem of predicting volatility in applied finance
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 pric...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2023-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2023/39/e3sconf_transsiberia2023_03022.pdf |
| Summary: | 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. |
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| ISSN: | 2267-1242 |