Risk Estimation in Energy Markets and Other Commodity Markets Using Value at Risk

Abstract

In this thesis, the historical model is compared to both the normal and student t distributions to find the best risk metric using Value at Risk (VaR). To investigate the diversification effect, six portfolios have been created; two portfolios of energy commodities, two portfolios of other non-energy commodities and two total portfolios that contain all of the assets. One portfolio from each segment gives all assets equal weight (balanced), while the other portfolio from each segment uses the allocation that provides the minimum variance.