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Forecasting oil prices volatility with Google searches
Název práce v češtině: Predikce volatility cen ropy pomocí Google Trends
Název v anglickém jazyce: Forecasting oil prices volatility with Google searches
Klíčová slova: Google Trends, ropa, VAR, vyhledávací dotaz, volatilita, nowcasting
Klíčová slova anglicky: Google Trends, oil, VAR, search query, price volatility, nowcasting
Akademický rok vypsání: 2016/2017
Typ práce: bakalářská práce
Jazyk práce: angličtina
Ústav: Institut ekonomických studií (23-IES)
Vedoucí / školitel: prof. PhDr. Ladislav Krištoufek, Ph.D.
Řešitel: skrytý - zadáno vedoucím/školitelem
Datum přihlášení: 13.06.2017
Datum zadání: 13.06.2017
Datum a čas obhajoby: 10.09.2019 09:00
Místo konání obhajoby: Opletalova - Opletalova 26, O206, Opletalova - místn. č. 206
Datum odevzdání elektronické podoby:31.07.2019
Datum proběhlé obhajoby: 10.09.2019
Oponenti: Dimitrios Zafeiris, M.Sc.
Kontrola URKUND:
Seznam odborné literatury
1. Bosler F. Models for oil price prediction and forecasting, http://sdsu-dspace.calstate.edu/handle/10211.10/433 (2010, accessed 17 October 2016).
2. Choi H, Varian HR (2012) Predicting the present with google trends. Econ Rec 88: 2–9. doi: 10.1111/j. 1475-4932.2012.00809.x
3. Pavlicek, J., & Kristoufek, L. (2015). Nowcasting Unemployment Rates with Google Searches: Evidence from the Visegrad Group Countries. PLoS ONE, 10(5), e0127084.
4. Preis T, Moat HS, Stanley HE (2013) Quantifying trading behavior in financial markets using google
trends. Sci Rep 3: 1684. doi: 10.1038/srep01684 PMID: 23619126
5. Curme C, Preis T, Stanley H, Moat H (2014) Quantifying the semantics of search behavior before stock
market moves. Proc Natl Acad Sci U S A 111: 11600–11605. doi: 10.1073/pnas.1324054111 PMID:
6. Da Z, Engelberg J, Gao P (2011) In search of attention. J Finance 66(5):1461–1499.
7. Ramos, S. B., Veiga, H., Latoeiro, P. (2013): Predictability of stock market activity using Google search queries, Statistics and Econometrics Working Papers, ws130605
Předběžná náplň práce v anglickém jazyce
Research question and motivation
Oil and its trade underlie international relations, so they are often a reflection of what is happening in the world. Oil markets are very dynamic. Often, changes in their prices depend on many geopolitical factors.
In recent decades, new methods of estimating and forecasting macroeconomic indicators have been developed.
In 2009, H. Choi and H.Varian put forward the hypothesis that the query statistics in Google should correlate with the current level of business activity, and it may also be useful for a short-term forecast. The idea is that a set of characteristic keywords is revealed, and then a graph is constructed based on the quantity of search queries. We immediately see how the public's interest in this sector is changing, and we can make assumptions about how the demand for corresponding shares will change in this connection.

1. Google searches data proves to be useful in short term forecasting of consumer behaviour.
2. The search query categories can be successfully utilized to nowcast oil prices volatility.

In recent years, oil has acquired the status of a "world currency", because the stability of the economy largely depends on it. The price of oil has become an important indicator of the state of the world economy. The main purpose of this work is to help to understand the market, whether it is possible to predict, and pre-warn changes in the oil market using Google searches. Relying on it, governments could competently adapt their international policy.
I will study the partial effect of searching for individual words using standard OLS time series method and vector auto-regression. Also I will test how these effects will be changed over time trough moving estimation window. For estimating prices volatility I will be using Garman-Klass estimator and CBOE Crude Oil Volatility Index.

1. Introduction
2. Literature Review & Theoretical Background
3. Methodology
4. Results
5. Conclusion
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