Journal ArticleDOI
Google search keywords that best predict energy price volatility
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TLDR
In this paper, the utility of Google search activity for energy related keywords are shown to be significant predictors of volatility by showing they have incremental predictive power beyond the conventional GARCH models in predicting volatility for energy commodities' prices.About:
This article is published in Energy Economics.The article was published on 2017-09-01. It has received 59 citations till now. The article focuses on the topics: Energy market & Volatility smile.read more
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Machine learning in energy economics and finance: A review
TL;DR: A review of the burgeoning literature dedicated to Energy Economics/Finance applications of ML suggests that Support Vector Machine, Artificial Neural Network, and Genetic Algorithms are among the most popular techniques used in energy economics papers.
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The dynamic dependence of fossil energy, investor sentiment and renewable energy stock markets
TL;DR: In this article, the authors investigated the dynamic directional information spillover of return and volatility between the fossil energy market, investor sentiment towards renewable energy and the renewable energy stock market using the connectedness network approach.
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Public attention to environmental issues and stock market returns
TL;DR: The authors empirically examined the effect of public attention to climate change and pollution on the weekly returns on US sustainability stock indices (i.e. the DJSI US and the FTSE4Good USA Index) in comparison to their conventional parent indices.
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Investor attention and crude oil prices: Evidence from nonlinear Granger causality tests
Sufang Li,Hu Zhang,Di Yuan +2 more
TL;DR: Using the Google search volume index (GSVI) to measure investor attention, Li et al. as mentioned in this paper investigated the relationship between investor attention and crude oil prices for the main crude oil markets worldwide.
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Novel hybrid model based on echo state neural network applied to the prediction of stock price return volatility
Gabriel Trierweiler Ribeiro,André A. P. Santos,André A. P. Santos,Viviana Cocco Mariani,Leandro dos Santos Coelho +4 more
TL;DR: A novel hybrid model is proposed, named HAR-PSO-ESN, which combines the feature design of the HAR model with the prediction power of ESN, and the consistent PSO metaheuristic approach for hyperparameters tuning, which produces more accurate predictions on most of the cases.
References
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Journal ArticleDOI
Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation
TL;DR: In this article, a new class of stochastic processes called autoregressive conditional heteroscedastic (ARCH) processes are introduced, which are mean zero, serially uncorrelated processes with nonconstant variances conditional on the past, but constant unconditional variances.
ReportDOI
A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix
Whitney K. Newey,Kenneth D. West +1 more
TL;DR: In this article, a simple method of calculating a heteroskedasticity and autocorrelation consistent covariance matrix that is positive semi-definite by construction is described.
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Generalized autoregressive conditional heteroskedasticity
Tim Bollerslev,Tim Bollerslev +1 more
TL;DR: In this paper, a natural generalization of the ARCH (Autoregressive Conditional Heteroskedastic) process introduced in 1982 to allow for past conditional variances in the current conditional variance equation is proposed.
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Investigating Causal Relations by Econometric Models and Cross-Spectral Methods
TL;DR: In this article, the cross spectrum between two variables can be decomposed into two parts, each relating to a single causal arm of a feedback situation, and measures of causal lag and causal strength can then be constructed.