We perform a large-scale empirical study in order to compare the forecasting performances of single-regime and Markov-switching () models from a risk management perspective. We find that models yield more accurate Value-at-Risk, expected shortfall, and left-tail distribution forecasts than their single-regime counterparts for daily, weekly, and ten-day equity log-returns. Also, our results indicate that accounting for parameter uncertainty improves the left-tail predictions, independently of the inclusion of the Markov-switching mechanism.