Prediction of Volatility and Value at Risk with Copulas for Portfolios of Commodities
Value at Risk (VaR) is a popular measurement for valuing the risk exposure. Correct estimates of VaR are essential in order to properly be able to monitor the risk. This thesis examines a copula approach for estimating VaR for portfolios of commodities. The predictions are made from a semi- parametric model with Monte Carlo methods. The underlying model is constructed by choosing the best fit from