ESSAYS ON INTERNATIONAL CRUDE OIL MARKETS AND ELECTRICITY SYSTEMS
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The dissertation studies two key commodities in the market, crude oil and electricity. Our goal in Chapter 2 is to analyze the crude oil futures recently introduced (March 2018) in Shanghai and the locational trading strategies they can provide. Features of the long-established reference indexes Brent, WTI as well as Oman are discussed. We then present the unique features of Shanghai crude oil futures in terms of delivery location and type of crude optionalities. Lastly, a locational arbitrage that is hedged against foreign exchange risk is designed around the Shanghai oil futures. In Chapter 3, we revisit and adjust the famous Theory of Storage, introduced by Kaldor (1939), to the unprecedented situation of WTI crude oil in 2020. The Theory of Storage describes features regularly observed in commodity markets, among which are the relationships between the size of inventory and the shape of the forward curve (Working, 1949) on one hand, and the volatility on the other. We propose and validate the “Reverse” Theory of Storage by replacing inventory with available storage capacity when storage is scarce. This property can be explained by the remarkable lack of storage that existed in Cushing, delivery point of WTI futures at that time and brought, for the first time in the history of oil markets, WTI prices to the value of minus 37 dollars. In Chapter 4, we introduce ‘Real option’ - a founding concept first coined by Majd and Pindyck (1987) - to the Distributed Energy Resources (DERs), in order to quantify the flexibility that the DERs can offer to the grid. We further define Swing Options, which have already been well traded in the natural gas market, to represent the DERs flexibility and price it in monetary value. We develop a novel Monte Carlo and Recurrent Neural Network (RNN) combining approach for the valuation of the Swing Option defined on two classes of DERs - smart water heaters and thermostats. Delta hedging strategies for the Swing Option are derived for the RNN valuation scheme.