This study compares two approaches for the Bayesian estimation of stochastic volatility (SV) models: a manual implementation of the Gibbs sampler with Metropolis–Hastings steps and an automated approach using the JAGS engine. Although both methods target the same posterior distribution, the existing literature offers very limited evidence on how general-purpose MCMC engines behave when they are applied to latent-volatility processes that exhibit strong persistence or heavy-tailed behavior. The present work addresses this gap by examining the convergence, mixing efficiency, and computational stability of both samplers across 13 currency return series, and by identifying the conditions under which automated sampling tends to deteriorate relative to a model-specific Gibbs–MH implementation. The results show that, although the manual sampler can produce more stable trajectories and more consistent convergence diagnostics, it generally requires significantly longer execution times than JAGS due to its more explicit structure and the large number of latent-state updates it performs. Taken together, the findings indicate that the manual sampler provides greater transparency and control over the inference process, whereas JAGS offers computational efficiency and reproducibility at the cost of reduced control over latent-state proposals. Beyond the empirical comparison, this study contributes a methodological perspective on the interaction between MCMC algorithms and the structural properties of SV models, clarifying important practical considerations for applied Bayesian inference.