  This study evaluates the practical usefulness of continuous-time arbitrage
strategies designed to exploit serial correlation in fractional financial
markets. Specifically, we revisit the strategies of \cite{Shiryaev1998} and
\cite{Salopek1998} and transfer them to a real-world setting by distretizing
their dynamics and introducing transaction costs. In Monte Carlo simulations
with various market and trading parameter settings, we show that both are
highly promising with respect to terminal portfolio values and loss
probabilities. These features and complementary sparsity make them valuable
additions to the toolkit of quantitative investors.
