Financial Option Volatility Prediction Based on Machine Learning Algorithm

Financial risks are increasing worldwide, thus finding suitable tools for financial risk management becomes particularly important. In this paper, the realized volatility and implied volatility of financial option data are calculated through the Black-Scholes option pricing model. Through the support vector machine in machine algorithm, the GARCH model is improved, the two-stage prediction method of GARCH-SVR model is constructed, and the volatility metric is set to examine the prediction effect of the model. Finally, a machine learning model is used to predict the implied volatility of CSI 300 ETF options to realize the prediction of volatility. The results show that the return of the arbitrage strategy constructed by the GARCH-SVR model, the maximum of which is 0.1858. Comparison with the other four models reveals that the machine learning predicts financial option volatility, which can bring better economic returns in the real market.