Historical Perspectives in Volatility Forecasting Methods with Machine Learning
Volatility forecasting for financial institutions plays a pivotal role across a wide range of domains, such as risk management, option pricing, and market making. For instance, banks can incorporate volatility forecasts into stress testing frameworks to ensure they are holding sufficient capital dur...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-05-01
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| Series: | Risks |
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| Online Access: | https://www.mdpi.com/2227-9091/13/5/98 |
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| author | Zhiang Qiu Clemens Kownatzki Fabien Scalzo Eun Sang Cha |
| author_facet | Zhiang Qiu Clemens Kownatzki Fabien Scalzo Eun Sang Cha |
| author_sort | Zhiang Qiu |
| collection | DOAJ |
| description | Volatility forecasting for financial institutions plays a pivotal role across a wide range of domains, such as risk management, option pricing, and market making. For instance, banks can incorporate volatility forecasts into stress testing frameworks to ensure they are holding sufficient capital during extreme market conditions. However, volatility forecasting is challenging because volatility can only be estimated, and different factors influence volatility, ranging from macroeconomic indicators to investor sentiments. While recent works show promising advances in machine learning and artificial intelligence for volatility forecasting, a comprehensive assessment of current statistical and learning-based methods is lacking. Thus, this paper aims to provide a comprehensive survey of the historical evolution of volatility forecasting with a comparative benchmark of key landmark models, such as implied volatility, GARCH, LSTM, and Transformer. We open-source our benchmark code to further research in learning-based methods for volatility forecasting. |
| format | Article |
| id | doaj-art-9fd192f275d9482da8fd7fb54c86b453 |
| institution | Kabale University |
| issn | 2227-9091 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Risks |
| spelling | doaj-art-9fd192f275d9482da8fd7fb54c86b4532025-08-20T03:48:01ZengMDPI AGRisks2227-90912025-05-011359810.3390/risks13050098Historical Perspectives in Volatility Forecasting Methods with Machine LearningZhiang Qiu0Clemens Kownatzki1Fabien Scalzo2Eun Sang Cha3Seaver College, Pepperdine University, Malibu, CA 90263, USAPepperdine Graziadio Business School, Pepperdine University, Malibu, CA 90263, USASeaver College, Pepperdine University, Malibu, CA 90263, USASeaver College, Pepperdine University, Malibu, CA 90263, USAVolatility forecasting for financial institutions plays a pivotal role across a wide range of domains, such as risk management, option pricing, and market making. For instance, banks can incorporate volatility forecasts into stress testing frameworks to ensure they are holding sufficient capital during extreme market conditions. However, volatility forecasting is challenging because volatility can only be estimated, and different factors influence volatility, ranging from macroeconomic indicators to investor sentiments. While recent works show promising advances in machine learning and artificial intelligence for volatility forecasting, a comprehensive assessment of current statistical and learning-based methods is lacking. Thus, this paper aims to provide a comprehensive survey of the historical evolution of volatility forecasting with a comparative benchmark of key landmark models, such as implied volatility, GARCH, LSTM, and Transformer. We open-source our benchmark code to further research in learning-based methods for volatility forecasting.https://www.mdpi.com/2227-9091/13/5/98volatility forecastingrisk managementdeep learningtime series analysisGARCHLSTM |
| spellingShingle | Zhiang Qiu Clemens Kownatzki Fabien Scalzo Eun Sang Cha Historical Perspectives in Volatility Forecasting Methods with Machine Learning Risks volatility forecasting risk management deep learning time series analysis GARCH LSTM |
| title | Historical Perspectives in Volatility Forecasting Methods with Machine Learning |
| title_full | Historical Perspectives in Volatility Forecasting Methods with Machine Learning |
| title_fullStr | Historical Perspectives in Volatility Forecasting Methods with Machine Learning |
| title_full_unstemmed | Historical Perspectives in Volatility Forecasting Methods with Machine Learning |
| title_short | Historical Perspectives in Volatility Forecasting Methods with Machine Learning |
| title_sort | historical perspectives in volatility forecasting methods with machine learning |
| topic | volatility forecasting risk management deep learning time series analysis GARCH LSTM |
| url | https://www.mdpi.com/2227-9091/13/5/98 |
| work_keys_str_mv | AT zhiangqiu historicalperspectivesinvolatilityforecastingmethodswithmachinelearning AT clemenskownatzki historicalperspectivesinvolatilityforecastingmethodswithmachinelearning AT fabienscalzo historicalperspectivesinvolatilityforecastingmethodswithmachinelearning AT eunsangcha historicalperspectivesinvolatilityforecastingmethodswithmachinelearning |