Unlike prior studies, this study examines the nonlinear, asymmetric and quantile effects of aggregate commodity index and gold prices on the price of Bitcoin. Using daily data from July 17, 2010 to February 2, 2017, we employed several advanced autoregressive distributed lag (ARDL) models. The nonlinear ARDL approach was applied to uncover short- and long-run asymmetries, whereas the quantile ARDL was applied to account for a second type of asymmetry, known as the distributional asymmetry according to the position of a dependent variable within its own distribution. Moreover, we extended the nonlinear ARDL to a quantile framework, leading to a richer new model, which allows testing for distributional asymmetry while accounting for short- and long-run asymmetries. Overall, our results indicate the possibility to predict Bitcoin price movements based on price information from the aggregate commodity index and gold prices. Importantly, we report the nuanced result that most often the relations between bitcoin and aggregate commodity, on the one hand, and between bitcoin and gold, on the other, are asymmetric, nonlinear, and quantiles-dependent, suggesting the need to apply non-standard cointegration models to uncover the complexity and hidden relations between Bitcoin and asset classes.