economypromЭкономика промышленности / Russian Journal of Industrial EconomicsRussian Journal of Industrial Economics2072-16332413-662XMISIS10.17073/2072-1633-2019-1-79-88economyprom-724Research ArticleФинансовый менеджментFinancial managementПрогнозирование реализованной волатильности котируемых российских акций с помощью инструмента Google Trends и вмененной волатильностиForecasting Realized Volatility of Russian stocks using Google Trends and Implied VolatilityБаженовТ. И.BazhenovT.

119049, Москва, ул. Шаболовка, д. 26, корп. 3.

26 Ul. Shabolovka, Moscow 119049.

ФантацциниД.FantazziniD.

PhD, канд. экон. наук, доцент, зам. заведующего кафедрой Эконометрики и математических методов в экономике.

119234, Москва, Ленинские Горы, д. 1, стр. 61.

1/61 Leninskie Gory, Moscow 119992.

dean.fantazzini@gmail.comМеждународный институт экономики и финансов НИУ ВШЭ.РоссияInternational College of Economics and Finance.Russian FederationМосковская школа экономики МГУ.РоссияMoscow School of Economics, Moscow State University.Russian Federation2019060420191217988Copyright © Баженов Т.И., Фантаццини Д., 20192019Баженов Т.И., Фантаццини Д.Bazhenov T., Fantazzini D.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://ecoprom.misis.ru/jour/article/view/724

Рассмотрено прогнозирование реализованной волатильности (Realized Volatility, RV) и стоимости под риском (Value-at-Risk, VaR) наиболее ликвидных российских акций с помощью моделей GARCH, ARFIMA и HAR, используя вменную волатильность (implied volatility), рассчитанную исходя из цен опционов, а также данные Google Trends. Анализ в пределах выборки показывает, что только вмененная волатильность оказывает существенное влияние на реализованную волатильность большинства акций, в то время как данные Google Trends не оказывают существенного влияния. Анализ за пределами выборки выявил, что модели, основанные на вмененной волатильности, ещё лучше прогнозируют реализованную волатильность, тогда как модели, построенные на активности интернет-запросов, в некоторых случая прогнозируют ещё хуже. Более того, простые модели HAR и ARFIMA без дополнительных регрессоров зачастую лучше прогнозируют дневную реализованную волатильность и дневную стоимость под риском на уровне 1 %, таким образом демонстрируя, что эффективность модели компенсирует возможные ошибки в спецификации модели и смещение параметров. Наши расчеты показывают, что, в случае, российских котируемых акций, данные Google Trends не несут дополнительной информации, не учтенной уже во вмененной волатильности.

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.

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The authors declare that there are no conflicts of interest present.