Forecasting Realized Volatility of Russian stocks using Google Trends and Implied Volatility
https://doi.org/10.17073/2072-1633-2019-1-79-88
Abstract
This work proposes to forecast the Realized Volatility (RV) and the Value-at-Risk (VaR) of the most liquid Russian stocks using GARCH, ARFIMA and HAR models, including both the implied volatility computed from options prices and Google Trends data. The in-sample analysis showed that only the implied volatility had a significant effect on the realized volatility across most stocks and estimated models, whereas Google Trends did not have any significant effect. The outof-sample analysis highlighted that models including the implied volatility improved their forecasting performances, whereas models including internet search activity worsened their performances in several cases. Moreover, simple HAR and ARFIMA models without additional regressors often reported the best forecasts for the daily realized volatility and for the daily Value-at-Risk at the 1 % probability level, thus showing that efficiency gains more than compensate any possible model misspecifications and parameters biases. Our empirical evidence shows that, in the case of Russian stocks, Google Trends does not capture any additional information already included in the implied volatility.
Keywords
forecasting,
realized volatility,
value-at-risk,
implied volatility,
google trends,
GARCH,
ARFIMA,
HAR
About the Authors
T. Bazhenov
International College of Economics and Finance.
Russian Federation
26 Ul. Shabolovka, Moscow 119049.
Russian Federation
26 Ul. Shabolovka, Moscow 119049.
D. Fantazzini
Moscow School of Economics, Moscow State University.
Russian Federation
1/61 Leninskie Gory, Moscow 119992.
Russian Federation
1/61 Leninskie Gory, Moscow 119992.
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Review
For citations:
Bazhenov T., Fantazzini D. Forecasting Realized Volatility of Russian stocks using Google Trends and Implied Volatility. Russian Journal of Industrial Economics. 2019;12(1):79-88. https://doi.org/10.17073/2072-1633-2019-1-79-88
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