The importance of being informed: forecasting market risk measures for the Russian RTS index future using online data and implied volatility over two decades
Dean Fantazzini and
Tamara Shangina ()
Additional contact information
Tamara Shangina: ICEF, National Research University Higher School of Economics, Moscow, Russian Federation
Applied Econometrics, 2019, vol. 55, 5-31
This paper focuses on the forecasting of market risk measures for the Russian RTS index future, and examines whether augmenting a large class of volatility models with implied volatility and Google Trends data improves the quality of the estimated risk measures. We considered a time sample of daily data from 2006 till 2019, which includes several episodes of large-scale turbulence in the Russian future market. We found that the predictive power of several models did not increase if these two variables were added, but actually decreased. The worst results were obtained when these two variables were added jointly and during periods of high volatility, when parameters estimates became very unstable. Moreover, several models augmented with these variables did not reach numerical convergence. Our empirical evidence shows that, in the case of Russian future markets, TGARCH models with implied volatility and Student’s t errors are better choices if robust market risk measures are of concern.
Keywords: forecasting; Value-at-Risk; realized volatility; Google trends; implied volatility; GARCH; ARFIMA; HAR; realized-GARCH (search for similar items in EconPapers)
JEL-codes: C22 C51 C53 G17 G32 (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
http://pe.cemi.rssi.ru/pe_2019_55_005-031.pdf Full text (application/pdf)
Working Paper: The importance of being informed: forecasting market risk measures for the Russian RTS index future using online data and implied volatility over two decades (2019)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:ris:apltrx:0372
Access Statistics for this article
Applied Econometrics is currently edited by Anatoly Peresetsky
More articles in Applied Econometrics from Russian Presidential Academy of National Economy and Public Administration (RANEPA)
Bibliographic data for series maintained by Anatoly Peresetsky ().