A sentiment-based approach to predict energy price volatility using distilRoBERTa and GARCH models
研究利用推特数据，通过distilRoBERTa模型提取情感，结合GARCH模型分析情感波动对原油和天然气价格短期波动的影响，发现情感波动显著影响能源价格波动，且对WTI原油存在非对称效应。
Previous studies have extensively examined the impact of information on short-term energy price fluctuations, using various forms to extract sentiment, such as search volume and news headlines. However, the influence of social media data on energy prices has received little attention. Therefore, we extend the existing literature by using tweets to analyze the impact of social media on the change in energy prices. Furthermore, we propose a new approach to classify text data using the distilRoBERTa fill-mask task, which provides direct predictions of classification keywords, rather than manually categorizing them as the traditional classification task does. The sentiment volatility then shows a significant impact on the volatility of the crude oil and natural gas prices, although an asymmetric effect is only observed for WTI crude oil. Our findings also indicate that the exponential GARCH model offers the best fit for energy price returns and sentiment volatility. In general, incorporating sentiment volatility enhances the performance of modeling the short-term volatility of crude oil and natural gas prices and suggests that social media seem to impact the uncertainty level and the expectation of customers and investors regarding energy prices. • The paper investigates the impact of sentiment derived from X on the fluctuation of energy prices. • The sentiment index is created by the distilRoBERTa fill-mask task and categorized into positive news (a price cut) and negative news (a price increase). • Sentiment volatility shows a significant impact on the volatility of energy prices, although an asymmetric effect is only observed for crude oil. • Social media seems to impact the uncertainty level and the expectation of customers and investors regarding energy prices.