How does market concern derived from the Internet affect oil prices
Jianfeng Guo,Qiang Ji +1 more
TLDR
In this article, the impact of short-and long-run market concerns, derived from search query volumes in Google for different domains around the oil market on oil volatility using co-integration and the modified EGARCH model was analyzed.About:
This article is published in Applied Energy.The article was published on 2013-12-01 and is currently open access. It has received 79 citations till now. The article focuses on the topics: Volatility (finance).read more
Citations
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Journal ArticleDOI
Dynamic connectedness and integration in cryptocurrency markets
TL;DR: In this paper, the authors apply a set of measures developed by Diebold and Yilmaz (2012) to examine connectedness via return and volatility spillovers across six large cryptocurrencies from August 7, 2015 to February 22, 2018.
Proceedings ArticleDOI
Detecting and Tracking the Spread of Astroturf Memes in Microblog Streams
Jacob Ratkiewicz,Michael Conover,Mark R. Meiss,Bruno Gonçalves,Snehal Patil,Alessandro Flammini,Filippo Menczer +6 more
TL;DR: In this article, the authors introduce an extensible framework that will enable the real-time analysis of meme diffusion in social media by mining, visualizing, mapping, classifying, and modeling massive streams of public microblogging events.
Journal ArticleDOI
Web search queries can predict stock market volumes.
Ilaria Bordino,Stefano Battiston,Guido Caldarelli,Guido Caldarelli,Matthieu Cristelli,Antti Ukkonen,Ingmar Weber +6 more
TL;DR: It is shown that daily trading volumes of stocks traded in NASDAQ-100 are correlated with daily volumes of queries related to the same stocks, and query volumes anticipate in many cases peaks of trading by one day or more.
Journal ArticleDOI
Oil price volatility and oil-related events: An Internet concern study perspective
Qiang Ji,Jianfeng Guo +1 more
TL;DR: In this paper, the effects of four types of oil-related events on world oil prices, using an event study methodology and an AR-GARCH model, were investigated using search query volumes in Google.
Journal ArticleDOI
Online big data-driven oil consumption forecasting with Google trends
TL;DR: The experimental study of global oil consumption prediction confirms that the proposed online big data-driven forecasting work with Google trends improves on the traditional techniques without Google trends significantly, for both directional and level predictions.
References
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Book ChapterDOI
Prospect theory: an analysis of decision under risk
Daniel Kahneman,Amos Tversky +1 more
TL;DR: In this paper, the authors present a critique of expected utility theory as a descriptive model of decision making under risk, and develop an alternative model, called prospect theory, in which value is assigned to gains and losses rather than to final assets and in which probabilities are replaced by decision weights.
Journal ArticleDOI
Prospect theory: analysis of decision under risk
Daniel Kahneman,Amos Tversky +1 more
Journal ArticleDOI
Maximum likelihood estimation and inference on cointegration — with applications to the demand for money
Søren Johansen,Katarina Juselius +1 more
TL;DR: In this paper, the estimation and testing of long-run relations in economic modeling are addressed, starting with a vector autoregressive (VAR) model, the hypothesis of cointegration is formulated as a hypothesis of reduced rank of the long run impact matrix.
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Introduction to Information Retrieval
TL;DR: In this article, the authors present an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections.
Journal ArticleDOI
Conditional heteroskedasticity in asset returns: a new approach
TL;DR: In this article, an exponential ARCH model is proposed to study volatility changes and the risk premium on the CRSP Value-Weighted Market Index from 1962 to 1987, which is an improvement over the widely-used GARCH model.