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Forecasting oil prices volatility with Google searches
Tolstoguzova, Ekaterina ; Krištoufek, Ladislav (advisor) ; Zafeiris, Dimitrios (referee)
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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Examining the relationships among cryptocurrencies using Google Trends
Heller, Michael ; Krištoufek, Ladislav (advisor) ; Džmuráňová, Hana (referee)
The topic of our thesis is the examination of the relationships among cryptocur- rencies using Google Trends. In our thesis, we concentrated on four cryptocur- rencies, namely: Bitcoin, Litecoin, Ethereum Classic and Ethereum. We obtained the data of daily opening prices, daily trading volumes and daily Google Trends queries in order to examine the relationships among the four cryptocurrencies. Applying the Vector autoregression model and Vector error correction model, we constructed four models. The first model contains only four time series of daily prices of cryptocurrencies. The second model is the first model enriched by the respective four time series of Google Trends queries. The third model contains the four time series of daily trading volumes of the four cryptocurrencies. The fourth model is the third model enriched by the four time series of Google Trends queries of respective cryptocurrencies. Then we applied the Impulse response analysis and the Forecast error variance decomposition in order to find some relationships among the variables. We found that there is some correlation among prices, volumes and Google Trends queries containing the names of the four cryptocurrencies. According to our results acquired by the Forecast error variance decomposition, in all our models, Bitcoin has the...
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Analysis of Price Determinants in the Art Market
Mizeráková, Elena ; Šopov, Boril (advisor) ; Moravcová, Hana (referee)
1 Abstract What qualities make the best-selling artworks worth so much? Does the in- terest of the general public influence the probability that the art will be sold in auction? The art market research focuses on various aspects that affect the potential of art as an investment. The boom of big data offers a unique op- portunity to utilize its global impact and improve the present models with a novel measure. Into the econometric analysis of auction results the thesis im- plements a change in the Internet searching volume provided by Google Trends as a reflection of the taste and the state of mind of society. The subject of the detailed discussion are not only the price determinants, but also the factors that affect the selling probability. The findings lead to a conclusion that the proposed measure based on Google Trends is significant for determining both, the odds of selling the artwork and its price. Beside that, an important effect on the price and the probability have auction houses, the personal brand of the artist or the medium of artwork. JEL Classification D44, C25, F23, Z10, Z11 Keywords art market, auctions, Google Trends, prices, price determinants, odds of selling Author's e-mail elena.mizerakova@gmail.com Supervisor's e-mail boril.sopov@gmail.com
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Predicting Stock Market Volatility with Google Trends
Pecháček, Jan ; Krištoufek, Ladislav (advisor) ; Janotík, Tomáš (referee)
This thesis aims to investigate the usability of Google Trends data for predicting stock market volatility. Using daily Google data on tickers of three companies with large market capitalization, we examine the causal relationship between Google data and volatility proxy. We employ two common models for volatility, Generalised Autoregressive Conditional Heteroskedasticity model (GARCH) and Heterogeneous Autoregressive model (HAR) and we augment them by adding Google data. We studied the performance of in-sample forecasting and out-sample forecasting. Our results show that Google data Granger-cause stock market volatility and is able to produce more accurate results in in-sample forecasts then models without Google data added.
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Google Econometrics: Unemployment in Visegrad Countries
Pavlíček, Jaroslav ; Krištoufek, Ladislav (advisor) ; Zeynalov, Ayaz (referee)
This thesis examines the relationship between job-related Google search query indices and unemployment rate in Visegrad countries. We found that the unemployment rate generally moves in the same direction as the search volume index for the job-related term. The series of Google search query indices also proved useful for prediction-making. Models with Google series showed lower MAE and RMSE of static forecast compared to base models in all four countries. However, only models for Poland and Slovakia showed potential for nowcasting. Powered by TCPDF (www.tcpdf.org)
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Vliv informační kaskády na sektorové indexy
Večeřa, Rudolf
Diploma thesis refers about effect of informational cascade, which is causing herding behaviour, in sector indices. Thesis distinguishes between market and sector informational cascade. Each of them is represented as an indicator made from dataset provided by Google trends service. Effect is demonstrated on extended version of CAPM theoretical concept to multi factorial model including the indicators, which is based on APT theoretical concept. For the purpose of robust analyses is then realized Granger causality on regression results.
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Forecasting Mortgages: Internet Search Data as a Proxy for Mortgage Credit Demand
Saxa, Branislav
This paper examines the usefulness of Google Trends data for forecasting mortgage lending in the Czech Republic. While the official monthly statistics on mortgage lending come with a publication lag of one month, the data on how often people search for mortgage-related terms on the internet are available without any lag on a weekly basis. Growth in searches for mortgages and growth in mortgages actually provided are strongly correlated. The lag between these two growth rates is two months. Evaluation of out-of-sample forecasts shows that internet search data improve mortgage lending predictions significantly. In addition to forecasting performance evaluation, an experimental indicator of restrictively tight mortgage credit standards and conditions is proposed. Nowadays many countries run bank lending surveys to monitor the tightness of bank lending standards and conditions. The proposed indicator represents a complementary tool to such a survey.
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