This paper examines the problematic of modeling crude oil price and volatility on two sets of data: a ten year time series with weekly observations and a five year series with daily observations. It begins by analyzing the fundamental characteristics of the product and by employing methods of descriptive statistics. After closely studying the dynamics of the series by testing for stationarity, seasonality and structural breaks, with the necessary transformations the data is prepared for modeling. Based on the selected information criterion, the optimal ARIMA model is chosen, from whoms characteristics are studied further. By choosing independent variables and testing for correlation, an optimal linear regression model is built which is then furtherly analyzed. As an addition to the ARIMA process, a basic GARCH model is built, followed by postestimation. Different variants of GARCH are also modeled: apARCH, eGARCH and gjrGARCH. Finally, different ARIMA models are compared based on the MSE and MEA criteria. The results don't show an advantage in choosing a concrete model, which therefore stresses the importance of choosing a simpler model, like AR(1). GARCH models are also compared based on the same criterion. The results show an advantage in using the apARCH model with emphasis on the ten year time series. The regular sGARCH is the second best alternative model choice. GARCH models show better results in modeling crude oil time series in comparison to ARIMA models because of the high volatility nature of the product.