  Rough Volterra volatility models are a progressive and promising field of
research in derivative pricing. Although rough fractional stochastic volatility
models already proved to be superior in real market data fitting, techniques
used in simulation of these models are still inefficient in terms of speed and
accuracy. This paper aims to present accurate and efficient tools and
techniques for Monte-Carlo simulations for a wide range of rough volatility
models. In particular, we compare three commonly used simulation methods: the
Cholesky method, the Hybrid scheme, and the rDonsker scheme. We also comment on
the implementation of variance reduction techniques. In particular, we show the
obstacles of the so-called turbocharging technique whose performance is
sometimes counter-productive. To overcome these obstacles, we suggest several
modifications.
