TRADING NATURAL GAS FUTURES THROUGH SIMULATION PREDICTIVE MODELING AND OPTIMIZATION

For many years, natural gas prices were strongly correlated with those of crude oil. Recently, natural gas prices started to show an independent trend. Natural gas prices are driven by the law of supply and demand which is reflected by the weather and inventory levels among other factors. In the last decade, electronic trading platforms took over the exchanges. With the advent of algorithm trading (AT) and in particular high-frequency trading (HFT), trading commodities, which include energy trading, became riskier due to their extremely volatile nature. This dissertation presents a novel framework that provides insight into the use of HFT in natural gas futures markets. Since there are no publicly disclosed data on such practices, the objective is to develop a comprehensive model for natural gas futures trading. A new heuristic simulation, predictive modeling and optimization algorithm that automates trading natural gas futures is proposed and evaluated. Simulation is used to reconstruct the order book using top of the book natural gas futures historical data. Predictive modeling techniques based on multi-class support vector machines are used to predict the occurrence and the amplitude of spread crossings. Finally, an inventory optimization model is used to determine optimal trading volumes for each trading period. Two types of trading strategies are derived: a strategy using Immediate-Or-Cancel orders where an order is totally or partially executed while the remaining is cancelled, and a strategy that limits orders’ cancellation. Both strategies are tested with real and synthetic data. In this setting, both strategies can lead to profit. This could be used by policymakers and market regulators to implement order cancellation restrictions on commodity futures trading to prevent harmful speculation

A Heuristic Simulation and Optimization Algorithm for Large Scale Natural Gas Storage Valuation under Uncertainty

Natural gas storage valuation is an optimal scheduling of natural gas storage facilities. It is a complex predictive decision making research problem since it involves the financial decisions and the physical storage facility characteristics. The challenge arises from large scale stochastic input data sets and complex mathematical models. Research in the literature has been heavily focused on the financial facet of the valuation with little emphasis on the physical storage facility characteristics. The mathematical models and the solution approaches provided in the literature so far are also either overly simplified or are only relevant for very small scale problems. The contribution of this research is on the physical storage facility characteristics in combination with the financial aspect of the natural gas storage valuation. A large scale stochastic non-linear natural gas storage valuation problem that includes underground and aboveground storage facilities is formulated and solved efficiently. A new heuristic simulation and optimization natural gas storage valuation algorithm that handles a very complex and large size problems is proposed. The algorithm (i) decreases significantly the computation time from hundreds of days to fractions of a second, (ii) provides a reasonable solution quality, and (iii) incorporates all the possible underground and aboveground physical gas storage facility complexities. The research has both practical applications and mathematical significance. Practically, natural gas storage facility managers can use the models developed in this research as decision support tools to make a predictive storage decision under uncertainty within a reasonable time. Mathematically, a novel perspective to solving a non-linear natural gas storage facilities valuation problem is provided. Such approach can be used in a variety of applications; for instance, the algorithm can be applied to a high penetration of renewables to electric power grid and fluid flow network optimization among others