Forecasting Volatility with Machine Learning and Rough Volatility: Example from the Crypto-Winter
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Acceso en línea: | |
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| _version_ | 1866910344756068352 |
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| author | Tang, Siu Hin Rosenbaum, Mathieu Zhou, Chao |
| author_facet | Tang, Siu Hin Rosenbaum, Mathieu Zhou, Chao |
| contents | We extend the application and test the performance of a recently introduced volatility prediction framework encompassing LSTM and rough volatility. Our asset class of interest is cryptocurrencies, at the beginning of the "crypto-winter" in 2022. We first show that to forecast volatility, a universal LSTM approach trained on a pool of assets outperforms traditional models. We then consider a parsimonious parametric model based on rough volatility and Zumbach effect. We obtain similar prediction performances with only five parameters whose values are non-asset-dependent. Our findings provide further evidence on the universality of the mechanisms underlying the volatility formation process. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_04727 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Forecasting Volatility with Machine Learning and Rough Volatility: Example from the Crypto-Winter Tang, Siu Hin Rosenbaum, Mathieu Zhou, Chao Statistical Finance Mathematical Finance Trading and Market Microstructure We extend the application and test the performance of a recently introduced volatility prediction framework encompassing LSTM and rough volatility. Our asset class of interest is cryptocurrencies, at the beginning of the "crypto-winter" in 2022. We first show that to forecast volatility, a universal LSTM approach trained on a pool of assets outperforms traditional models. We then consider a parsimonious parametric model based on rough volatility and Zumbach effect. We obtain similar prediction performances with only five parameters whose values are non-asset-dependent. Our findings provide further evidence on the universality of the mechanisms underlying the volatility formation process. |
| title | Forecasting Volatility with Machine Learning and Rough Volatility: Example from the Crypto-Winter |
| topic | Statistical Finance Mathematical Finance Trading and Market Microstructure |
| url | https://arxiv.org/abs/2311.04727 |