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.