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In the deregulated power industry, a generation company (GenCo) sells energy and ancillary services primarily through auctions in a daily market. Developing effective strategies to optimize hourly offer curves for a hydrothermal power system to maximize profits has been one of the most challenging and important tasks for a GenCo. This paper presents an integrated bidding and scheduling algorithm with risk management under a deregulated market. A stochastic mixed-integer optimization formulation having a separable structure with respect to individual units is first established. A method combining Lagrangian relaxation and stochastic dynamic programming is then presented to select hourly offer curves for both energy and reserve markets. In view that pumped-storage units provide significant energy and reserve at generating and pumping, the offering strategies are specially highlighted in this paper. Numerical testing based on an 11-unit system with a major pumped-storage unit in the New England market shows that the algorithm is computationally efficient, and effective energy and reserve offer curves are obtained in 4-5 min on a 600-MHz Pentium III PC. The risk management method significantly reduces profit variances and, thus, bidding risks.