Liquidity effects on oil volatility forecasting: From fintech perspective.
Fin-tech is an emerging field, inspiring revolutionary innovations in the financial field. It may initiate the evolutionary episode of the financial research, where volatility forecasting is a crucial topic in finance. For forecasting volatility, GARCH model is a prevailing model, however, further i...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-01-01
|
| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0260289 |
| _version_ | 1818943247215493120 |
|---|---|
| author | Shusheng Ding Tianxiang Cui Yongmin Zhang Jiawei Li |
| author_facet | Shusheng Ding Tianxiang Cui Yongmin Zhang Jiawei Li |
| author_sort | Shusheng Ding |
| collection | DOAJ |
| description | Fin-tech is an emerging field, inspiring revolutionary innovations in the financial field. It may initiate the evolutionary episode of the financial research, where volatility forecasting is a crucial topic in finance. For forecasting volatility, GARCH model is a prevailing model, however, further improvement of the GARCH model is still challenging. In this paper, we demonstrate how Fintech can play a part in volatility forecasting by employing a metaheuristic procedure called Genetic Programming. On the basis, we are able to develop a new volatility forecasting model, which can beat GARCH family models (including GARCH, IGARCH and TGARCH models) in a significant way. Since genetic programming is an evolutionary algorithm based on the principles of natural selection, this innovative work will be a breakthrough point in the financial area. The innovation of this paper demonstrates how GP technology can be applied in the financial field, attempting to explore the volatility forecasting area from the combination of new technology and finance, known as fintech. More importantly, when the formula of volatility forecasting is unknown as we introduce a new factor, namely, the liquidity factor, we unveil that how GP method can be helpful in determining the specific volatility forecasting model format. We thereby exhibit the liquidity effects on volatility forecasting filed from the fintech perspective. |
| first_indexed | 2024-12-20T07:24:17Z |
| format | Article |
| id | doaj.art-4847422c205f44afa95bf357f2d3d75f |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-20T07:24:17Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-4847422c205f44afa95bf357f2d3d75f2022-12-21T19:48:34ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-011611e026028910.1371/journal.pone.0260289Liquidity effects on oil volatility forecasting: From fintech perspective.Shusheng DingTianxiang CuiYongmin ZhangJiawei LiFin-tech is an emerging field, inspiring revolutionary innovations in the financial field. It may initiate the evolutionary episode of the financial research, where volatility forecasting is a crucial topic in finance. For forecasting volatility, GARCH model is a prevailing model, however, further improvement of the GARCH model is still challenging. In this paper, we demonstrate how Fintech can play a part in volatility forecasting by employing a metaheuristic procedure called Genetic Programming. On the basis, we are able to develop a new volatility forecasting model, which can beat GARCH family models (including GARCH, IGARCH and TGARCH models) in a significant way. Since genetic programming is an evolutionary algorithm based on the principles of natural selection, this innovative work will be a breakthrough point in the financial area. The innovation of this paper demonstrates how GP technology can be applied in the financial field, attempting to explore the volatility forecasting area from the combination of new technology and finance, known as fintech. More importantly, when the formula of volatility forecasting is unknown as we introduce a new factor, namely, the liquidity factor, we unveil that how GP method can be helpful in determining the specific volatility forecasting model format. We thereby exhibit the liquidity effects on volatility forecasting filed from the fintech perspective.https://doi.org/10.1371/journal.pone.0260289 |
| spellingShingle | Shusheng Ding Tianxiang Cui Yongmin Zhang Jiawei Li Liquidity effects on oil volatility forecasting: From fintech perspective. PLoS ONE |
| title | Liquidity effects on oil volatility forecasting: From fintech perspective. |
| title_full | Liquidity effects on oil volatility forecasting: From fintech perspective. |
| title_fullStr | Liquidity effects on oil volatility forecasting: From fintech perspective. |
| title_full_unstemmed | Liquidity effects on oil volatility forecasting: From fintech perspective. |
| title_short | Liquidity effects on oil volatility forecasting: From fintech perspective. |
| title_sort | liquidity effects on oil volatility forecasting from fintech perspective |
| url | https://doi.org/10.1371/journal.pone.0260289 |
| work_keys_str_mv | AT shushengding liquidityeffectsonoilvolatilityforecastingfromfintechperspective AT tianxiangcui liquidityeffectsonoilvolatilityforecastingfromfintechperspective AT yongminzhang liquidityeffectsonoilvolatilityforecastingfromfintechperspective AT jiaweili liquidityeffectsonoilvolatilityforecastingfromfintechperspective |