A useful way to generate high dynamic range(HDR) video is to capture a sequence of images with alternate exposures and then resort to interpolation techniques that can help to reconstruct missing information at each timestamp. However, doing video frame interpolation is not that easy as fast motion in the scene is not effectively interpolated which can lead to ghosting artefacts. There are a new class of sensors called event sensors which asynchronously measures per-pixel brightness changes and when used offer advantages like high temporal resolution, high dynamic range, low latency etc. For HDR video reconstruction, we propose to use hybrid cameras that capture alternate exposure low dynamic range images as well as high-speed events that will be helpful to do event-based interpolation, as opposed to using other non-event based interpolation techniques to give better temporally coherent frames. Thus, the auxiliary visual information provided by them in any intermediate time between frames can be helpful for reconstructing high-speed videos and this property can be helpful in dynamic scenarios. So we took up the method of event-based interpolation using simulated high-speed events data to generate high quality intermediate frames of different exposures at each instant and then used deep learning based algorithms for HDR reconstruction. We also compared the results with those of non-event based interpolation methods and found that event-based techniques perform better when a large number of frames are interpolated.