  We obtain sharp sufficient conditions for exponentially integrable stochastic
processes $X=\{X(t)\!\!: t\in [0,1]\}$, to have sample paths with bounded
$\Phi$-variation. When $X$ is moreover Gaussian, we also provide a bound of the
expectation of the associated $\Phi$-variation norm of $X$. For an Hermite
process $X$ of order $m\in \N$ and of Hurst index $H\in (1/2,1)$, we show that
$X$ is of bounded $\Phi$-variation where $\Phi(x)=x^{1/H}(\log(\log
1/x))^{-m/(2H)}$, and that this $\Phi$ is optimal. This shows that in terms of
$\Phi$-variation, the Rosenblatt process (corresponding to $m=2$) has more
rough sample paths than the fractional Brownian motion (corresponding to
$m=1$).
