Volatility Prediction and Risk Management: An SVR-GARCH Approach


Karasan A., Gaygisiz E.

The Journal of Financial Data Science, vol.2, no.4, pp.85-104, 2020 (Peer-Reviewed Journal) identifier

  • Publication Type: Article / Article
  • Volume: 2 Issue: 4
  • Publication Date: 2020
  • Doi Number: 10.3905/jfds.2020.1.046
  • Journal Name: The Journal of Financial Data Science
  • Page Numbers: pp.85-104

Abstract

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.