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Summary In oil and gas markets, the relationships between the spot and futures prices reveal important opportunities for value creation. When oil prices are in contango (i.e., when futures prices are higher than the expected future spot prices), it may be profitable for a trader to hold oil in storage and enter into a futures contract instead of selling oil in the spot market. The decision to either sell oil in the spot market or use the storage to sell oil in the future is usually challenging because the future spot prices and futures prices are uncertain. In this paper, we discuss the storage trading decisions by use of a realistic example, and we propose an analysis methodology on the basis of a two-factor price process for modeling spot and futures oil prices. The dynamic decision problem, sell spot or sell forward, is analyzed with a forward dynamic optimization algorithm and the least-squares Monte Carlo simulation.
Summary Although a severe drop in commodity prices was expected to adversely affect a financially leveraged producer, the variability of this effect across the universe of exploration and production (E&P) companies during the current downturn, which began in Autumn 2014, surprised industry participants. What factors caused the effect to be magnified for certain companies and muted for others? In examining 71 public E&P companies, we found a moderate correlation between a company's financial leverage and the loss of its equity value. Our study confirms that leveraged producers are exposed to the risk of debt-induced value loss in a downturn. Two other factors were studied for their influence on equity performance: the economics of a company's resource portfolio and the extent of its commodity-price hedging. To examine the effect of resource economics, we analyzed the finding-and-development (F&D) cost of producers and noticed statistically significant differences by hydrocarbon-producing regions. When controlled for the regional effect, the correlation between a company's financial leverage and the loss of its equity value substantially improved. The offsetting effect of a superior resource economics on the debt-induced value loss was evident. For example, the producers operating in the Permian Basin outperformed their similarly leveraged peers operating in the Williston Basin, a less-profitable region. A subset of financially leveraged companies significantly outperformed their similarly leveraged peers. These outperformers, termed here as “Leaders,” showcase a strong alignment between their financing and hedging activities. We observed evidence of the use of a continuous-hedging program through the price cycle by the Leaders. They responded soon to changes in their hedge positions, such as hedge roll-offs, rather than hedging when it is advantageous to do so. A key benefit of such a continuous-hedging program is the dollar-cost averaging of hedged prices, which appears to be an implicit goal of the Leaders. We contend that a leveraged producer must coordinate its hedging and financing policies to maintain an alignment between the hedged volume and the debt load. Endogeneity arises in the relationship between debt and hedges, with each influencing the other. An alignment can be achieved through the implementation of a continuous-hedging program factoring in annual production, operating cashflows, financial leverage, and hedged volumes. This paper takes the hedging debate for a leveraged producer beyond the realm of “to hedge or not to hedge” and addresses the question of “how much to hedge.”
Summary When exploring nearby prospects in a common area, the outcome of drilling a well can change the chance of success in nearby prospects, affecting their economics and drilling decisions. Here, besides possibly discovering hydrocarbons, a single well could also supply information about other wells. For such a cluster of exploration prospects, which well should we drill first, and which next? More importantly, what is the economic value of this group of prospects? The answers are multidimensional; they depend, at least, on geological dependencies and economic dynamics. Because it takes time to interpret each drilling outcome and update our understanding regarding neighboring prospects, the varying hydrocarbon prices also affect the economics of the upcoming wells. Therefore, our sequence of drilling decisions should consider both geological dependencies and uncertainty in prices. In this paper, we develop a valuation model for a group of interdependent prospects. We use a dynamic programming model that combines the binomial representation of prices with the conditional probability of success or failure at each drilling site. The software implementation of the algorithm accompanies this paper and is a useful valuation and decision‐support system.
We could have borrowed from Gabriel Garcia Marquez's Love in the Time of Cholera to title this piece, "Oil & Gas in the Time of Coronavirus." But that would only recognize the coronavirus disease of 2019 (COVID-19) and its impact on oil demand and ignore the black swan in the concurrent supply shock posed by Russia's refusal to hyphenate itself with OPEC again in cutting oil production. Broader uncertainties from an election year in the US and growing investor clamor for shale profitability and energy transition initiatives add further, even if now less-urgent, uncertainties. We at ADI instead see the "perfect storm" as a better metaphor for the collective impact from the Russian-Saudi spat and COVID-19. Metaphors and clever writing aside, how should we think about this perfect storm?
Abstract The fluctuation of crude oil price is one of the greatest risks that oil-field operators are faced with. Crude oil is a strategic natural resource, and the range of the price fluctuation is quite wide. To avoid the disadvantage caused by the price fluctuation, operators can use oil-linked bonds, such as a crude oil future and a crude oil option on the market. Swap contract is the portfolio of crude oil futures, which is currently utilized in a static manner. Swap contract is preferred, if one wants to minimize the risk. However, this type of contract cannot respond to the boom of oil price and eventually loses the chance of bonus. In this paper, a dynamic hedging strategy is proposed, which can respond to the boom of oil price. The proposed portfolio changes its components in response to the changes of oil market. The performance of the strategy is assessed by using the real trading history on the New York Mercantile Exchange over the period of ten years. Introduction The future contract is an agreement to buy or sell an asset at a certain time in the future for a certain price. Any kind of asset, such as index of stocks, foreign currency, interest rate, and any kinds of commodities are dealt on the exchanges all over the world. One of the parties to a future contract assumes a long position and agrees to buy the asset for a certain specified price. The other party assumes a short position and agrees to sell the asset for the same price. Future contracts are commonly used to hedge the risk caused by the fluctuation of asset price. By virtue of this contract, a person who has a plan to sell an asset at a certain time in the future can determine his income. A crude oil producer, for instance, can determine his income at the time he plans to produce crude oil. However, taking a long position on 100% quantity of the asset determined to sell sometimes leads to a large loss of income compared with the no hedge case. Ederington applied a modern portfolio theory to calculate an optimal ratio. Ederington's portfolio is static, and does not allow us to take any action against the daily fluctuation of crude oil price on the market. It contains the future of only one fixed maturity date, which implies that a flexible action cannot be taken. In this paper, we propose "Dynamic Portfolio," analyzing a crude oil market every week, and optimizing a hedge ratio every week. This portfolio contains 1-month future to 12-month future. 250-week market data prior to the action date are analyzed, and the ratio of portfolio is optimized weekly. The data used in this paper are obtained from Bloomberg and DataStream. Ederington's Portfolio One difference between the Ederington's hedging portfolio model and more familiar portfolio models is that cash and futures market holdings are not viewed as substantitutes. In his portfolio, spot market holdings are fixed and the quantity of stock is to be decided. This portfolio contains a single future with the hedge ratio of b, aiming at maximizing a utility function. Expectancy and variance of the portfolio return are given by eq.1 and eq.2, respectively. Equation (1) Equation (2) where ?S and ?f are the changes in spot price, S, and future price, f, during a period of time equal to the life of the hedge, respectively. ss and sf are the standard deviations of ?S and ?f, respectively. ? is the correlation coefficient between ?S and ?f.