Effectiveness of Artificial Neural Networks in Hedging against WTI Crude Oil Price Risk

Abstract

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.

Using Artificial Neural Networks to Support the Decision-Making Process of Buying Call Options Considering Risk Appetite

“…This allows option buyers to plan their future budget without exposing themselves to unforeseen price movements, such as those that the buyers of futures must accommodate, since margin account requirements change with the price of oil, which can affect total cost. It is also worth noting that for options, this amount is known, and it is up to the buyer to decide whether or not to accept this upfront cost [49].…”

“…In our study, we focus on WTI crude oil call options available on the New York Mercantile Exchange (NYMEX). The analyses were carried out on the same data as the authors' previous studies [48,49], from 16 June 2009 to 14 February 2020, so that the results of various approaches could be compared. We include only ATM (at-the-money) options, i.e., those with a strike price identical (or very close) to the WTI future price on a given day.…”

“…These options expire 3 business days before the underlying futures contract (CL). The analyzed European Style option contracts are settled in cash on the expiration day [49,57]. The payout from this transaction is the settlement price of the CL contract minus the strike price, multiplied by a thousand barrels, or zero, whichever is greater [57].…”

“…We kept the assumption from our previous studies [48,49] that only one option contract may be bought per day-this was done to be able to compare results. However, the performance indicators were modified to take into account different levels of risk appetite.…”

“…Numerous studies [38][39][40][41][42][43][44][45][46][47] show that ANNs are an effective tool for forecasting crude oil prices. However, the aforementioned studies focused mostly on oil price predictions, which do not necessarily lead to successful investment strategies as shown by the authors in previous studies [48,49] (and others [50]). This paper builds on an approach proposed by the authors in these studies to analyze the effect of an ANN parameter expressing risk appetite.…”