Work detail

Forecasting oil prices volatility with Google searches

Author: Bc. Ekaterina Tolstoguzova
Year: 2019 - summer
Leaders: prof. PhDr. Ladislav Krištoufek Ph.D.
Work type: Bachelors
Language: English
Pages: 62
Awards and prizes:
Abstract: Oil market pricing is highly susceptible to geopolitical and economic events.
With the rapid development of information technology, energy market can
quickly get external information shocks through the Internet. This thesis
examines the relationship between prices of three oil benchmarks, CBOE
Crude Oil Volatility Index, and Google search queries. We built VAR model to
study Granger causality and to provide impulse response analysis. Results
indicate both one side and two-side causal relationship between oil-related
series and most of the search queries. Out-of sample forecasting with
measures of predictive accuracy and Diebold-Mariano test demonstrated that
Google trends can improve short-run prediction potential only for models with
WTI price and volatility index.




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