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Forecasting Realized Volatility of Russian stocks using Google Trends and Implied Volatility

Timofey Bazhenov and Dean Fantazzini

MPRA Paper from University Library of Munich, Germany

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 out-of-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 (search for similar items in EconPapers)
JEL-codes: C22 C51 C53 G17 G32 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-big, nep-cis, nep-ets, nep-for, nep-ore and nep-rmg
Date: 2019-04
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Published in Russian Journal of Industrial Economics 12.1(2019): pp. 79-88

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Journal Article: Forecasting Realized Volatility of Russian stocks using Google Trends and Implied Volatility (2019) Downloads
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