Value-at-Risk in the energy sector
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?