Andersen, T. G., Bollerslev, T., & Diebold, F. X. (2007). Roughing it up: Including jump components in the measurement, modeling, and forecasting of return volatility. Review of Economics and Statistics, 89, 701–720.
Aouadi, A., Arouri, M., & Teulon, F. (2013). Investor attention and stock market activity: Evidence from France. Economic Modelling, 35, 674–681.
Bank, M., Larch, M., & Peter, G. (2011). Google search volume and its influence on liquidity and returns of German stocks. Financial Markets and Portfolio Management, 25, 239–264.
Barber, B., & Odean, T. (2001). The internet and the investor. Journal of Economic Perspectives, 15, 41–54.
Barndorff-Nielsen, O. E., & Shephard, N. (2002). Econometric analysis of realized volatility and its use in estimating stochastic volatility models. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 64, 253–280.
Bauwens, L., Hafner, C., & Laurent, S. (2012). Handbook of volatility models and their applications. Wiley handbooks in financial engineering and econometrics. Hoboken: Wiley.
Bijl, L., Kringhaug, G., Molnár, P., & Sandvik, E. (2016). Google searches and stock returns. International Review of Financial Analysis, 45, 150–156.
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
Bollerslev, T., Patton, A. J., & Quaedvlieg, R. (2016). Exploiting the errors: A simple approach for improved volatility forecasting. Journal of Econometrics, 192, 1–18.
Caporin, M., & McAleer, M. (2014). Robust ranking of multivariate GARCH models by problem dimension. Computational Statistics and Data Analysis, 76, 172–185.
Choi, H., & Varian, H. (2012). Predicting the present with Google Trends. Economic Record, 88, 2–9.
Corsi, F. (2009). A simple approximate long-memory model of realized volatility. Journal of Financial Econometrics, 7, 174–196.
Da, Z., Engelberg, J., & Gao, P. (2011). In search of attention. The Journal of Finance, 66, 1461–1499.
Dimpfl, T., & Jank, S. (2016). Can internet search queries help to predict stock market volatility? European Financial Management, 22, 171–192.
Griffiths, T. L., & Steyvers, M. (2004). Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America, 101, 5228–5235.
Hansen, P. R., & Lunde, A. (2005). A realized variance for the whole day based on intermittent high-frequency data. Journal of Financial Econometrics, 3, 525–554.
Iwata, T., Yamada, T., Sakurai, Y. & Ueda, N. (2010). Online multiscale dynamic topic models. In Proceedings of the 16th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 663–672). KDD ’10. New York, NY: ACM.
Joseph, K., Wintoki, M. B., & Zhang, Z. (2011). Forecasting abnormal stock returns and trading volume using investor sentiment: Evidence from online search. International Journal of Forecasting, 27, 1116–1127.
Kim, S.-H., & Kim, D. (2014). Investor sentiment from internet message postings and the predictability of stock returns. Journal of Economic Behavior and Organization, 107, 708–729.
Masuda, H., & Morimoto, T. (2012). Optimal weight for realized variance based on intermittent high-frequency data. Japanese Economic Review, 63, 497–527.
McAleer, M., & Medeiros, M. C. (2008). Realized volatility: A review. Econometric Reviews, 27, 10–45.
Minka, T. P. (2000). Estimating a Dirichlet distribution. Technical report, Microsoft Research
Mitra, G., & Mitra, L. (2011). The handbook of news analytics in finance. The Wiley finance series. Hoboken: Wiley.
Moat, H. S., Preis, T., Olivola, C. Y., Liu, C., & Chater, N. (2014). Using big data to predict collective behavior in the real world. Behavioral and Brain Sciences, 37, 92–93.
Nardo, M., Petracco-Giudici, M., & Naltsidis, M. (2016). Walking down wall street with a tablet: A survey of stock market predictions using the web. Journal of Economic Surveys, 30, 356–369.
Noureldin, D., Shephard, N., & Sheppard, K. (2012). Multivariate high-frequency-based volatility (heavy) models. Journal of Applied Econometrics, 27, 907–933.
Patton, A. J. (2011). Data-based ranking of realised volatility estimators. Journal of Econometrics, 161, 284–303.
Siganos, A., Vagenas-Nanos, E., & Verwijmeren, P. (2014). Facebook’s daily sentiment and international stock markets. Journal of Economic Behavior and Organization, 107, 730–743.
Smith, G. P. (2012). Google internet search activity and volatility prediction in the market for foreign currency. Finance Research Letters, 9, 103–110.
Takeda, F., & Wakao, T. (2014). Google search intensity and its relationship with returns and trading volume of Japanese stocks. Pacific-Basin Finance Journal, 27, 1–18.
Tetlock, P. C. (2007). Giving content to investor sentiment: The role of media in the stock market. The Journal of Finance, 62, 1139–1168.
Varian, H. R. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28, 3–28.
Vlastakis, N., & Markellos, R. N. (2012). Information demand and stock market volatility. Journal of Banking and Finance, 36, 1808–1821.
Vozlyublennaia, N. (2014). Investor attention, index performance, and return predictability. Journal of Banking and Finance, 41, 17–35.