Participants in energy markets make sequential decisions across multiple time horizons under uncertainty, leading to large-scale multistage stochastic optimization problems. Stochastic dual dynamic programming is widely used for its tractability, but its application to modern energy markets is challenged by nested dependencies induced by participation across multiple interrelated markets under increasing uncertainty from distributed energy resources. To address this, we introduce a stage-wise hybrid nested Benders’ decomposition-stochastic dual dynamic programming approach by nodalizing early-stage uncertainties. We apply the approach to a bidding and operation problem for a virtual power plant in Day-ahead and Intraday electricity markets, where strong nested dependencies exist between Day-ahead prices and Intraday uncertainties. We reformulate the problem by incorporating bidding decisions into the state space and by introducing a penalty term in the final stage to address recourse infeasibility. Numerical experiments on a high-renewable system demonstrate that the proposed method converges and outperforms conventional approaches. Furthermore, the performance gains over stochastic dual dynamic programming increase as the level of the nested dependency increases.