Long memory and crude oil’s price predictability

Abstract

This paper discusses the usefulness of the long term memory property in price prediction. In particular, the Hurst's exponents related to a wide set of portfolios generated by three crude oils are estimated by using the detrended fluctuation analysis. To this aim, the daily empirical data on West Texas Intermediate, Brent crude oil and Dubai crude oil for a period of more than ten years have been considered. It is shown that specific combinations are associated to persistence/antipersistence long-run behaviors, and this highlights the presence of statistical arbitrage opportunities. Such an outcome shows that long term memory can effectively serve as price predictor.

Mean-Reverting Statistical Arbitrage Strategies in Crude Oil Markets

“…When we deal with commodity markets, we refer to crude oil price dynamics, such as, for example, in Kristoufek and Vosvrda (2014), who analysed long-run dependence phenomena for prices empirically. More recently, we mention Cerqueti et al (2019), Nakajima (2019) and Cerqueti and Fanelli (2021), who investigated long-run equilibria and statistical arbitrages in commodity markets, and additional literature, such as He et al (2023), Poutre et al (2023), and Zhang et al (2024.…”

“…On the contrary, in statistical arbitrage, the mispricings on the market are based on the expected value of the assets, that is, the mispricing of price relationships are true in expectation, in the long run. Cerqueti et al (2019) and Cerqueti and Fanelli (2021) develop and apply some methodologies for analysing long-run equilibrium among commodities.…”

Persistence in complex systems

“…Crude oil and derived prices and markets long-term persistence analysis has been discussed in the literature, with the usual techniques already discussed in this peper, such as Hurst coefficient estimation, DFA, MF-DFA and related approaches. Studies on general crude oil markets persistence [263,262,261,260,259,264] and specific national markets such as Chinese [265] or Brazilian [266] can be found in the literature. Long-term persistence evaluation techniques have also been recently applied to the study of other markets such as housing [269] or agricultural [267,268].…”

Point and interval prediction of crude oil futures prices based on chaos theory and multiobjective slime mold algorithm

“…The forecasting of the COP is also a challenging task because the COP can be influenced by multiple factors, including inflation, exchange rates, supply and demand, international politics, wars, and pandemics (Chai et al, 2018 ; You et al, 2021 ). COP time series have many characteristics, including intrinsic nonlinearity, randomness, sudden structural changes, volatility, and a chaotic nature (Cerqueti and Fanelli, 2021 ; Zhao et al, 2021 ). With the continuous development of machine learning (ML) models and optimization algorithms, the prediction of COP has become increasingly accurate.…”