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

T Bazhenov,D Fantazzini

doi: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.

Highlights

Volatility forecasting is of cardinal importance in several applications, from derivatives pricing to portfolio and risk management, see Bauwens et al [1] for a large survey
This paper aims to estimate the predictive power of online search activity and implied volatility for forecasting the realized volatility of several Russian stocks
Three volatility forecasting models and several different specifications, including the implied volatility computed from option prices and Google Trends data, were used to model and forecast the realized volatility and the VaR of four Russian stocks

Summary

Introduction

Volatility forecasting is of cardinal importance in several applications, from derivatives pricing to portfolio and risk management, see Bauwens et al [1] for a large survey. This paper aims to estimate the predictive power of online search activity (as proxied by Google Trends data) and implied volatility (computed from option prices) for forecasting the realized volatility of several Russian stocks. In this regard, the implied volatility measures the investors’ sentiment about the future performance of an asset, see the survey of Mayhew [6] and references therein for more details. The models’ volatility forecasts are employed to compute the Value-at-Risk (VaR) for each asset to measure their market risk

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Conclusion