Oil prices experienced extraordinary plummets from 2008 to 2009 and 2014 to 2016. During these short periods, world oil prices crashed by more than one half. Many economic analysts ascribed the decline during the period 2008–۲۰۰۹ to the global financial crisis. They believed that the 2008 financial crisis resulted in a worldwide economic slump, which, in turn, undercut international oil prices. Later on, numerous industry experts and academics asserted that amassed supplies of oil resulted in tumbling prices during the period 2014–۲۰۱۶٫ In an attempt to designate the specific supply factors that are responsible for the decline, both U.S. shale oil development and increasing crude oil production of OPEC countries have been suggested. Moreover, there have been various other arguments used to explain the causes of unusual oil price declines. However, to the best of our knowledge, no study has conducted a thorough empirical investigation regarding these causes. Certainly, establishing the cause(s) of the recent collapses by rigorous evidence will provide meaningful information for both economic policymakers and scholars. Our research findings via a novel and rigorous approach suggested in this article may contribute to the work of professionals in both spheres. This article proposes a new approach to verify the above views by examining the relation between demand and supply factors, and oil price dynamics, via some predictive models. In particular, the supply and demand factors of price determinants are examined to explain the causes of the plummets. We propose applying novel autoregressive models with exogenous variables reflecting real demand, speculative demand, and supply factors for forecasting monthly global crude oil prices during the periods of decline. Specifically, we build four different types of predictive models: (1) with only real demand factors, (2) with real and speculative demand factors, (3) with real demand and supply factors, and (4) with real and speculative demand and supply factors. We evaluate these models and suggest which factors are the most important in explaining the oil price drops during the two aforementioned periods. First, we analyze the period of falling global oil prices (June 2008–February 2009). We then check whether the findings during the 2008 oil price fall were still observable during June 2014–February 2016, when prices collapsed again. Due to the backdrops of in-sample forecasting evaluation, such as sensitivity to outliers and model misspecifications (Inoue and Kilian, 2004; Panopoulou and Pantelidis, 2015), we utilize the out-ofsample forecasting approach in the evaluation of candidate models. The empirical forecasting results are retrospectively evaluated to identify the factors enabling improved forecasting performance.