Climate change compels the development and enforcement of policies and regulations designed to diminish carbon emissions, imposing substantial implications on the energy sector. Given the contribution of crude oil prices to carbon emissions, developing precise forecasting methods is imperative. However, existing studies often overlook the inherent uncertainty in price movements by focusing solely on point forecasting. To address this limitation, this paper constructs a threshold autoregressive interval-valued model with interval sentiment indexes for climate change (TARIX) to analyze and forecast interval-valued crude oil prices. We have found that the interval climate sentiment index, derived from social media, can significantly enhance the accuracy in forecasting interval crude oil prices. Moreover, we propose an interval-based trading strategy that can effectively reduce volatility and enhance returns. Our empirical results demonstrate that our intervalvalued forecast model outperforms traditional forecasting methods in terms of forecasting accuracy and profit generation.

Publication:

Energy Economics Volume 134, June 2024

https://doi.org/10.1016/j.eneco.2024.107612

Author:

Zishu Cheng, Mingchen Li, Yuying Sun, Yongmiao Hong, Shouyang Wang

Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China

School of Economics and Management, University of Chinese Academy of Sciences, China

Center for Forecasting Science, Chinese Academy of Sciences, China

Email:yongliu@lsec.cc.ac.cn