The fast-growing literature on the news and social media analysis provide empirical evidence that the financial markets are often driven by information rather than facts. However, the direct effects of sentiments on the returns are of main interest. In this paper, we propose to study the cross-industry influence of the news for a set of US and European stocks. The graphical Granger causality of the news sentiments-excess return networks is estimated by applying the adaptive Lasso procedure. We introduce two characteristics to measure the influence of the news coming from each sector and analyze their dynamics for a period of 10 years ranging from 2005 to 2014. The results obtained provide insight into the news spillover effects among the industries and the importance of sentiments related to certain sectors during periods of financial instability.

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This paper introduces the concept of the realized hierarchical Archimedean copula (rHAC). The proposed approach inherits the ability of the copula to capture the dependencies among financial time series, and combines it with additional information contained in high-frequency data. The considered model does not suffer from the curse of dimensionality, and is able to accurately predict high-dimensional distributions. This flexibility is obtained by using a hierarchical structure in the copula. The time variability of the model is provided by daily forecasts of the realized correlation matrix, which is used to estimate the structure and the parameters of the rHAC. Extensive simulation studies show the validity of the estimator based on this realized correlation matrix, and its performance, in comparison to the benchmark models. The application of the estimator to one-day-ahead Value at Risk (VaR) prediction using high-frequency data exhibits good forecasting properties for a multivariate portfolio.

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The fast-growing literature on the news and social media analysis provide empirical evidence that the financial markets are often driven by information rather than facts. However, the direct effects of sentiments on the returns are of main interest. In this paper, we propose to study the cross-industry influence of the news for a set of US and European stocks. The graphical Granger causality of the news sentiments-excess return networks is estimated by applying the adaptive Lasso procedure. We introduce two characteristics to measure the influence of the news coming from each sector and analyze their dynamics for a period of 10 years ranging from 2005 to 2014. The results obtained provide insight into the news spillover effects among the industries and the importance of sentiments related to certain sectors during periods of financial instability.