PT  - JOURNAL ARTICLE
AU  - Abdullah Karasan
AU  - Esma Gaygisiz
TI  - Volatility Prediction and Risk Management: <em>An SVR-GARCH Approach</em>
AID  - 10.3905/jfds.2020.1.046
DP  - 2020 Oct 31
TA  - The Journal of Financial Data Science
PG  - 85--104
VI  - 2
IP  - 4
4099  - https://pm-research.com/content/2/4/85.short
4100  - https://pm-research.com/content/2/4/85.full
AB  - This 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.TOPICS: Big data/machine learning, risk management, simulations, statistical methods, VAR and use of alternative risk measures of trading risk, volatility measuresKey Findings• Machine learning–based implementations in finance can lead to improved performance.• Volatility prediction based on the SVR-GARCH machine learning–based volatility prediction model outperforms traditional volatility prediction models, making it possible to have more accurate financial models.• Using volatility prediction in the value-at-risk model yields far better results, implying that, given the better-performing volatility model, it is likely to manage financial risk better than ever.