Risk management is used by firms to translate the risks associated to their business activities into competitive advantages. One of the most widely used risk measures is Value-at-Risk (VaR), defined as the maximum loss of a portfolio within a given time horizon and at a given level of confidence. In this paper, copula functions are used to forecast the VaR of an equally weighted portfolio comprising a small cap stock index and a large cap stock index for the oil and gas industry. The following empirical/research questions have been analyzed: (i) is it worth modelling nonnormalities that characterize financial data when forecasting VaR? (ii) Do forecasts from flexible models outperform those from simple models?