Risk management of commodity prices is an important yet challenging task. Given the complex behaviour of commodity prices, this creates the need of using sophisticated models of commodity prices dynamics. Obviously, parameter estimation of such models poses another challenge. Previous literature has addressed this problem using Markov Chain Monte Carlo, which is computationally expensive for parameter estimation and inference. In this paper we develop an efficient Maximum Likelihood Estimation procedure based on the characteristic function. We then estimate parameters a stochastic volatility model with stochastic drift utilizing the time-series of rice and coffee prices. We show that such model produces realistic distributions of both commodity prices. Finally, using the estimated model parameters we calculate various risk measures such as Value at Risk or Expected Shortfall.