Volatility forecasting with machine learning and intraday commonality
We apply machine learning models to forecast intraday realized volatility (RV), by exploiting commonality in intraday volatility via pooling stock data together, and by incorporating a proxy for the market volatility. Neural networks dominate linear regressions and tree-based models in terms of perf...
| Main Authors: | , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Oxford University Press
2023
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| _version_ | 1811139129284493312 |
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| author | Zhang, C Zhang, Y Cucuringu, M Qian, Z |
| author_facet | Zhang, C Zhang, Y Cucuringu, M Qian, Z |
| author_sort | Zhang, C |
| collection | OXFORD |
| description | We apply machine learning models to forecast intraday realized volatility (RV), by exploiting commonality in intraday volatility via pooling stock data together, and by incorporating a proxy for the market volatility. Neural networks dominate linear regressions and tree-based models in terms of performance, due to their ability to uncover and model complex latent interactions among variables. Our findings remain robust when we apply trained models to new stocks that have not been included in the training set, thus providing new empirical evidence for a universal volatility mechanism among stocks. Finally, we propose a new approach to forecasting 1-day-ahead RVs using past intraday RVs as predictors, and highlight interesting time-of-day effects that aid the forecasting mechanism. The results demonstrate that the proposed methodology yields superior out-of-sample forecasts over a strong set of traditional baselines that only rely on past daily RVs. |
| first_indexed | 2024-03-07T07:49:24Z |
| format | Journal article |
| id | oxford-uuid:09619aca-a61b-465a-931f-feaacf50e880 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:01:11Z |
| publishDate | 2023 |
| publisher | Oxford University Press |
| record_format | dspace |
| spelling | oxford-uuid:09619aca-a61b-465a-931f-feaacf50e8802024-05-07T11:07:50ZVolatility forecasting with machine learning and intraday commonalityJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:09619aca-a61b-465a-931f-feaacf50e880EnglishSymplectic ElementsOxford University Press2023Zhang, CZhang, YCucuringu, MQian, ZWe apply machine learning models to forecast intraday realized volatility (RV), by exploiting commonality in intraday volatility via pooling stock data together, and by incorporating a proxy for the market volatility. Neural networks dominate linear regressions and tree-based models in terms of performance, due to their ability to uncover and model complex latent interactions among variables. Our findings remain robust when we apply trained models to new stocks that have not been included in the training set, thus providing new empirical evidence for a universal volatility mechanism among stocks. Finally, we propose a new approach to forecasting 1-day-ahead RVs using past intraday RVs as predictors, and highlight interesting time-of-day effects that aid the forecasting mechanism. The results demonstrate that the proposed methodology yields superior out-of-sample forecasts over a strong set of traditional baselines that only rely on past daily RVs. |
| spellingShingle | Zhang, C Zhang, Y Cucuringu, M Qian, Z Volatility forecasting with machine learning and intraday commonality |
| title | Volatility forecasting with machine learning and intraday commonality |
| title_full | Volatility forecasting with machine learning and intraday commonality |
| title_fullStr | Volatility forecasting with machine learning and intraday commonality |
| title_full_unstemmed | Volatility forecasting with machine learning and intraday commonality |
| title_short | Volatility forecasting with machine learning and intraday commonality |
| title_sort | volatility forecasting with machine learning and intraday commonality |
| work_keys_str_mv | AT zhangc volatilityforecastingwithmachinelearningandintradaycommonality AT zhangy volatilityforecastingwithmachinelearningandintradaycommonality AT cucuringum volatilityforecastingwithmachinelearningandintradaycommonality AT qianz volatilityforecastingwithmachinelearningandintradaycommonality |