Although speculative activity is central to black markets for currency, the out-of-sample performance of structural models in those settings is unknown. We substantially update the literature on empirical determinants of black market rates and evaluate the out-of-sample performance of linear models and non-parametric Bayesian treed Gaussian process (BTGP) models against the random walk benchmark. Fundamentals-based models outperform the benchmark in out-of-sample prediction accuracy and trading rule profitability measures given future values of fundamentals. In simulated real-time trading exercises, however, the BTGP achieves superior realized profitability, accuracy and market timing, while linear models do no better than a random walk.