Realized Volatility Forecasting for Russian Stocks Using Google Trends

Student: Bazhenov Timofey

Faculty: International College of Economics and Finance

Educational Programme: Financial Economics (Master)

Year of Graduation: 2018

This paper models and predicts the Value-at-Risk and Realized Volatility of most liquid Russian stocks using GARCH, ARFIMA and HAR-RV models which include Implied volatility of this stocks and investors’ interest to Stock market of Russia measured in the basis of the Google Trends database. The sample consists of four stocks with data from 04/01/2016 to 19/04/2018. It was found that Implied volatility and Google Trends are important: the predictive power of models increases after adding them in model separately. However, the relationship between Realized Volatility and Internet search activity is statistically insignificant when Implied volatility is accounted for. Therefore, Google Trends do not increase predictive power of models and do not capture some additional information.

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