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One of the risks given in the introduction
One of the major risks that the energy market players face is price fluctuations that characterise the industry. The prices of petroleum products keep on changing, which exposes the stakeholders to the risks of losing their revenues. The price variations are attributed to the unpredictable demand and supply forces (Hobbs and Meier, 2012). One way to mitigate the risk emanating from variations in the prices of natural gases is hedging.
Hedging in the energy market involves selling a futures contract by the producer at the current market price. A fall in the price of the concerned hedged product shall not limit the profits of the producer in the short-term. In case the prices fall below the market price, the producer receives compensation from the hedging bank for the difference in the revenues caused by the low price (Wang and Wu, 2012). Therefore, the producer realises a sustainable income from the hedged product despite price variations.
The nature of the hedge
The changes in the price of the natural gases are twofold, [it may rise or fall] and thus, a short-term hedging plan is recommendable. As opposed to the long-term plan, the short-term hedging arrangement allows the producer to “lock-in” the prices of the concerned product for a short period (Pinson et al., 2009). Additionally, under the short-term hedging plan, the producer can buy back the futures contract in case the prices of the product falls. To illustrate how hedging can mitigate the risks associated with price fluctuations, this section gives an example of natural gas hedging.
Assuming that an independent power producing company anticipates excess production of natural gas and that the price of the product is expected to fall in the near future, the company shall sell a short-term future contract to avoid the losses. If the company arranges to hedge 10,000 Mmbtu for the next one month, it shall sell one futures contract at the current market price, say $4.015/MMbtu. If the price of the natural gas falls in the month, the bank shall compensate the producer for the lost income (Mercatus Energy Advisors, 2011). The seller may also buy back the futures contract if the prices fall, say at $3.515/Mmbtu. In such a scenario, the producer shall earn a profit of $0.50/Mmbtu ($4.015/Mmbtu-$3.515/Mmbtu). Therefore, the company’s losses arising from the price differentials shall be minimal.
Discuss the implications of a failure of the hedging strategy
Inasmuch as hedging may be an effective tool of minimising price fluctuations related risks, it is important to align the business objectives with the hedging plan. The managers should specify the objective of the hedging plan and consider other market forces that may have an impact on profits. It is important to note that hedging only minimises the risks associated with price variations, and it does not address other risks associated with political and climatic conditions that may equally affect the profitability of the business.
A change in the legislation, such as the enactment of laws to protect the environment by restricting the amount of carbon dioxide emissions, may cause diminished sales, thus, absorbing the gains derived from the hedge arrangement (Azevedo et al., 2007). Similarly, adverse weather conditions may limit the production and supply of the hedged product, which reverses the gains achieved from the hedge plan.
Azevedo, F., Vale, Z. A. and de Moura Oliveira, P. B. (2007) ‘A decision-support system based on particle swarm optimisation for multiperiod hedging in electricity markets’, IEEE transactions on power systems, 22(3), pp. 995-1003.
Hobbs, B. F. and Meier, P. (2012) Energy decisions and the environment: a guide to the use of multicriteria methods. Springer.
Mercatus Energy Advisors: Energy Hedging-Back to the basics Part 1- Futures (2011)
Pinson, P., Papaefthymiou, G., Klockl, B. and Verboomen, J. (2009) Dynamic sizing of energy storage for hedging wind power forecast uncertainty. IEEE.
Wang, Y. and Wu, C. (2012) ‘Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models’, Energy Economics, 34(6), pp. 2167-2181.