Forecasting volatility with empirical similarity and Google Trends

Perceived vs Actual Financial Crisis and Bank Credit Standards: Is There Any Indication of Self-Fulfilling Prophecy?

“…The relevant bibliography covers a various set of economic issues. To be more precise, many researchers employ google queries as an indicator of public sentiment to examine several aspects of economic activity such as: employment/unemployment (Askitas and Zimmermann, 2009;Choi and Varian, 2009;Fondeur and Karamé, 2013), economic prediction (Choi and Varian, 2012), private consumption (Penna and Huang, 2009, Vosen and Schmidt 2011, 2012, growth cycle analysis (Suhoy, 2009), volatility market phases (Hamid and Heiden, 2015), stock-trading (Takeda and Wakao, 2014), house sales (Wu and Brynjolfsson, 2015) and tourism (Siliverstovs and Wochner, 2018). We now turn our attention to the explanatory variables that we employ, a short description of which can be found at Table 2.…”

Time Varying Spillovers between the Online Search Volume and Stock Returns: Case of CESEE Markets

“…The author examined the vector approach due to findings that not only the investor's attention causes return and risk series, but a bi-directional relationship is found in this research. Hamid and Heiden (2015) also focused on the DJIA index by utilizing the VAR, ARMA (AutoRegressive Moving Average) and HAR (heterogeneous AR) models, with a focus on risk forecasting. Again, the reciprocal causality was found here as well (time span: January 2004-October 2013).…”

“…However, the majority of existing research utilizes two common approaches: The first one being that the online search volume is being used as a predictor in a combination of ARIMA-GARCH 2 model of stock return and/or volatility series; and the second one is using the VAR 3 framework in order to identify the Granger causality test results. However, Vozlyublennaia (2014) and Hamid and Heiden (2015) have shown that there exists a bi-directional relationship between investor's attention and stock returns. Moreover, Bijl et al (2016) introduced dynamics by calculating rolling returns and stock betas with the investor's attention included in the model.…”

Building News Measures from Textual Data and an Application to Volatility Forecasting

“…Recent studies have shown that online search activity is also associated with volatility and returns in the financial, commodity, and exchange-rate markets. (See Da et al (2011) and Vlastakis and Markellos (2012) for individual stocks; Andrei and Hasler (2015), Dimpfl and Jank (2016), and Hamid and Heiden (2015) for stock indexes; Vozlyublennaia (2014) for stock and bond indices, gold, and crude oil; Da et al (2015) for stock indices, the VIX volatility index, and equity and Treasury bonds mutual funds; Guo and Ji (2013) for crude oil; and Smith (2012) and Goddard et al (2015) for exchange rates.) 6 A Form 10-K is an annual report required by the U.S. Securities and Exchange Commission (SEC), that gives a comprehensive summary of a company's financial performance.…”