Accurately forecasting gasoline volatility is significant for risk management,economic analysis,and option pricing formulas for future contracts.This study proposes a novel interval-valued hierarchical decomposition a...Accurately forecasting gasoline volatility is significant for risk management,economic analysis,and option pricing formulas for future contracts.This study proposes a novel interval-valued hierarchical decomposition and ensemble(IHDE)approach to investigate gasoline price volatility.Our interval-based IHDE method can decompose the complex price process into different components to capture the distinct features of each component,which is helpful for forecasting and analyzing complex price processes.By using interval-valued data,the dynamics of gasoline prices in terms of levels and variations can be fully utilized in this study.Fully utilizing the informational gain of interval-valued data improves forecasting performance.In forecasting weekly gasoline volatility,we document that the proposed IHDE approach outperforms the GARCH,EGARCH,CARR,and ACI models,indicating the importance of capturing features of different frequency components and utilizing the informational gain of interval-valued data for gasoline volatility forecasts.展开更多
基金Supported by National Natural Science Foundation of China(72322016,72073126,71988101)Beijing Natural Science Foundation(9254024)。
文摘Accurately forecasting gasoline volatility is significant for risk management,economic analysis,and option pricing formulas for future contracts.This study proposes a novel interval-valued hierarchical decomposition and ensemble(IHDE)approach to investigate gasoline price volatility.Our interval-based IHDE method can decompose the complex price process into different components to capture the distinct features of each component,which is helpful for forecasting and analyzing complex price processes.By using interval-valued data,the dynamics of gasoline prices in terms of levels and variations can be fully utilized in this study.Fully utilizing the informational gain of interval-valued data improves forecasting performance.In forecasting weekly gasoline volatility,we document that the proposed IHDE approach outperforms the GARCH,EGARCH,CARR,and ACI models,indicating the importance of capturing features of different frequency components and utilizing the informational gain of interval-valued data for gasoline volatility forecasts.
