Long memory and crude oil’s price predictability

Roy Cerqueti,Viviana Fanelli

doi:10.1007/s10479-019-03376-y

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 10 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.

Highlights

A great strand of literature on time series deals with the analysis of the so-called persistence or long term memory property
The formalization of the concept of long term memory property was defined by Hurst (1951) for the specific case of hydrological time series
Some interesting results have been here obtained: first, a wide part of mispricing portfolios exhibits an antipersistent long-run behavior, with Hurst’s exponent H < 0.5; second, we have shown the existence of some portfolios following a geometric Brownian motion, which is strongly connected to the presence of statistical arbitrage opportunities; third, the Hurst’s exponents of the portfolios vary with an unexpected regularity as the quotes of portfolio change; fourth, in no cases one can observe noteworthy long-run persistence of the related portfolios, confirming the mean-reverting nature of the commodities portfolios prices; fifth, the simulated trajectories when H = 0.5 represent a replication of the observed ones with one time lag

Summary

Introduction

A great strand of literature on time series deals with the analysis of the so-called persistence or long term memory property. Alvarez-Ramirez et al (2008) empirically find evidences of long-run autocorrelations in crude oil markets towards efficiencies and they analyze short-term autocorrelations on the basis of the estimation of the Hurst’s exponent dynamics.

Results

Conclusion