An AI Model for Oil Volatility Forecasting

By introducing a genetic algorithm learning with a classifier system, we construct an AI model for oil volatility forecasting on the basis of Internal Information and External Information. The model provides decision support for mark-to-market portfolio and risk management by forecasting whether 1-day-ahead volatility is above a given threshold. Moreover, we explore the dynamic influencing mechanism of different types of information through information usage frequency in the learning process. In particular, we find that the jump component of oil realized volatility is efficient only in bull market, and currency information contributes most rather than oil information in bear market. Therefore, this article provides an AI method to forecast oil volatility as well as to improve the information structure of forecasting models.