Volatility analysis for the GARCH-Ito model with option data

Low-frequency historical data, high-frequency historical data, and option data are three primary sources that can be used to forecast an underlying security's volatility. In this article, we propose an explicit model integrating the three information sources. Instead of directly using option price data, we extract option-implied volatility from option data and estimate its dynamics. We provide joint quasimaximum likelihood estimators for the parameters and establish their asymptotic properties. Real data examples demonstrate that the proposed model has better out-of-sample volatility forecasting performance than other popular volatility models.

Automated summary

In this article, a model that integrates low-frequency historical data, high-frequency historical data, and option data for volatility forecasting is proposed. The model uses option-implied volatility extracted from option data and demonstrates better out-of-sample performance compared to other popular volatility models.