Despite the growing share of renewable energy sources, most of the world energy supply is still based on hydrocarbons and the vast majority of world transport is fuelled by oil products. Thus, the profitability of many companies may depend on the effective management of oil price risk. In this article, we analysed the effectiveness of artificial neural networks in hedging against the risk of WTI crude oil prices increase. This was reformulated from a regressive problem to a classification problem. The effectiveness of our approach, using artificial neural networks to classify observations, was verified for over ten years of WTI futures quotes, starting from 2009. The data analysis presented in this paper confirmed that the buyer of a call option was more often likely to incur a loss as a result of its purchase than make a profit after the final payoff from the call option. The results of the conducted research confirm that neural networks can be an effective form of protection against the risk of price fluctuations. The effectiveness of a network’s operation depends on the choice of assessment indicators, but analyses show that the networks which, for the indicator that was selected, gave the best results for the training set, also resulted in positive rates of return for the test set. Significantly, we also showed interdependence between seemingly unrelated indicators: percentage of the best possible results achieved in the analysed period of time by the proposed method and percentage of all available call options that were purchased based on the results from the networks that were used.
Oil price changes significantly influence proper functioning of the entire world economy, which entails the risk of losses. One of the possible ways to reduce this risk is to use some dedicated risk management tools, such as options contracts. In this paper we investigate the possibility of using multilayer perceptron neural networks to provide signals of long positions to take in the European call options. The experiments conducted on the West Texas Intermediate (WTI) oil prices (2630 observations coming from 16 June 2009 until 14 February 2020) allowed the selection of the network parameters, such as the activation function or the network error measure, giving the highest return on options contracts. Despite the fact that about 2/3 call options produced losses, the buying signals provided by the network for the test set allowed it to reach a positive return value. This indicates that neural networks can be a useful tool supporting the process of managing the risk of changes in oil prices using option contracts.
This study aims to examine the impact of selected market parameters of the European crude oil options on the hedging costs and break-even points (BEPs) in the long strap strategy. The paper analyses the impact of the following market parameters: Volatility and the future price of crude oil, the strike price and time to expiration. The theoretical aspect consisted in using the black model to calculate the value of the option price and the long strap strategy BEP in the condition of ever-changing market parameters. These calculations, by determining implied volatilities of the options, have been adapted to the actual data from the exchange market for the options on WTI futures contract. It was made possible owing to the quik strike platform made available by a CME group exchange. To obtain information about the impact of volatility, time and price of futures on the costs of hedging and BEPs in the long strap strategy, the authors calculated the Greeks (delta, gamma, vega and theta) for the crude oil options. Having done that, not only could they determine the direction but also the power of impact that the parameters had on the final results in the long strap strategy.
This paper focuses on the factors that influence the changes in copper prices. A brief review of the literature showed several areas of interests of researchers, such as global economic situation and development of Asian countries. This paper draws attention to concurrent price trends, some pairs of raw materials (e.g. copper – aluminum, copper – silver, even copper – palladium). This paper uses multiple regression model containing monthly data spanning from January 2012 to June 2015, which consist of 42 observations for metals: copper, zinc, aluminum, silver, gold, platinum and palladium. And it shows a strong relationship between the prices of copper and other metals.
The high volatility of commodity prices and various problems that the energy sector has to deal with in the era of COVID-19 have significantly increased the risk of oil price changes. These changes are of the main concern of companies for which oil is the main input in the production process, and therefore oil price determines the production costs. The main goal of this paper is to discover decision rules for a buyer of American WTI (West Texas Intermediate) crude oil call options. The presented research uses factors characterizing the option price, such as implied volatility and option sensitivity factors (delta, gamma, vega, and theta, known as “Greeks”). The performed analysis covers the years 2008–2022 and options with an exercise period up to three months. The decision rules are discovered using association analysis and are evaluated in terms of the three investment efficiency indicators: total payoff, average payoff, and return on investment. The results show the existence of certain ranges of the analyzed parameters for which the mentioned efficiency indicators reached particularly high values. The relationships discovered and recorded in the form of decision rules can be effectively used or adapted by practitioners to support their decisions in oil price risk management.
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