Traditional cameras face limitations in maintaining focus across dynamic scenes, especially during rapid motion, due to the constraints of their lenses. Post-capture refocusing techniques, including deep learning-based methods and light field cameras, have been explored to mitigate these challenges. However, these approaches frequently struggle with temporal consistency or experience a trade-off in spatial resolution. In this paper, we introduce the coded event focal stack, a novel approach that captures both motion and depth information through event streams recorded during a modulated focal sweep. Our coded event focal stack enables the generation of full-time intermediate frames refocused at arbitrary focal distances. Extensive experiments on both synthetic and real-world datasets demonstrate the superior refocusing capability of our method over state-of-the-art techniques, particularly in dynamic scenes with complex motion and depth variations.