Google search keywords that best predict energy price volatility

Mohamad Afkhami, Lindsey Cormack and Hamed Ghoddusi

Energy Economics, 2017, vol. 67, issue C, 17-27

Abstract: Internet search activity data has been widely used as an instrument to approximate trader attention in different markets. This method has proven effective in predicting market indices in the short-term. However, little attention has been paid to demonstrating search activity for keywords that best grab investor attention in different markets. This study attempts to build the best practically possible proxy for attention in the market for energy commodities using Google search data. Specifically, we confirm the utility of Google search activity for energy related keywords are significant predictors of volatility by showing they have incremental predictive power beyond the conventional GARCH models in predicting volatility for energy commodities' prices. Starting with a set of ninety terms used in the energy sector, the study uses a multistage filtering process to create combinations of keywords that best predict the volatility of crude oil (Brent and West Texas Intermediate), conventional gasoline (New York Harbor and US Gulf Coast), heating oil (New York Harbor), and natural gas prices. For each commodity, combinations that enhance GARCH most effectively are established as proxies of attention. The results indicate investor attention is widely reflected in Internet search activities and demonstrate search data for what keywords best reveal the direction of concern and attention in energy markets.

Keywords: Google search activity; Energy market; Volatility prediction; Energy price volatility (search for similar items in EconPapers)
JEL-codes: B23 B26 G12 G17 Q43 Q47 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (61)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:eneeco:v:67:y:2017:i:c:p:17-27

DOI: 10.1016/j.eneco.2017.07.014

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