In this paper, we describe and compare two statistical parsing approaches for the hybrid dependency-constituency syntactic representation used in the Quranic Arabic Treebank (Dukes and Buckwalter, 2010). In our first approach, we apply a multi-step process in which we use a shift-reduce algorithm trained on a pure dependency preprocessed version of the treebank. After parsing, the dependency output is converted into the hybrid representation. This is compared to a novel one-step parser that is able to learn the hybrid representation without preprocessing. We define an extended labelled attachment score (ELAS) as our performance metric for hybrid parsing, and report 87.47% (F1 score) for the multi-step approach, and 89.03% (F1 score) for the one-step integrated algorithm. We also consider the effect of using different sets of morphological features for parsing the Quran, comparing our results to recent work on Modern Standard Arabic.