Forecasting oil prices volatility with Google searches
|Author:||Bc. Ekaterina Tolstoguzova|
|Year:||2019 - summer|
|Leaders:|| doc. PhDr. Ladislav Krištoufek Ph.D.
|Work type:|| Bachelors
|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.