Volatility Prediction and Risk Management: An SVR-GARCH Approach

Date

2020-10-01

Author

Karasan, Abdullah
Gaygısız Lajunen, Esma

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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his study aims first at improving volatility prediction using a machine learning model called support vector regression GARCH (SVR-GARCH) using selected 30 stocks listed on the S&P 500. The authors compare the prediction results of the SVR-GARCH model with the GARCH family models and find that SVR-GARCH outperforms these models based on the performance metrics. The second goal of this study is to calculate value-at-risk (VaR) using predictions obtained in the previous part. Moreover, backtesting is applied to check the accuracy of the VaR results. The findings suggest that using predictions obtained from the SVR-GARCH model boosts VaR calculations and hence provides better financial risk management.

URI

https://hdl.handle.net/11511/93592

Journal

The Journal of Financial Data Science

DOI

https://doi.org/10.3905/jfds.2020.1.046

Collections

Department of Economics, Article

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Citation Formats

A. Karasan and E. Gaygısız Lajunen, “Volatility Prediction and Risk Management: An SVR-GARCH Approach,” The Journal of Financial Data Science, vol. 2, no. 4, pp. 85–104, 2020, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/93592.