Title: Exploring the resilience of crude oil market via nonlinear dynamics and wavelet-based analysis: an international experience

Authors: Emmanuel Senyo Fianu

Addresses: University of Naples Parthenope, Via Ammiraglio Ferdinando Acton, 38, 80133, Naples, Italy; Leuphana University of Lüneburg, Universitätsallee 1, 21335, Lueneburg, Germany; University of Verona, Via dell'Artigliere 19, 37129 Verona, Italy

Abstract: This paper investigates a signal modality analysis for the characterisation and detection of nonlinearity in crude oil markets. Given the nonlinear and time-varying characteristics of international crude oil prices, this study employs the recently proposed delay vector variance (DVV) method that examines local predictability of a signal in the phase space to detect the determinism and nonlinearity in a time series. In addition, wavelet transforms, which have recently emerged as a mathematical tool for multi-resolution decomposition of signals, is utilised. In particular, among the wavelet methodologies considered, the complex Morlet wavelet is useful and best at detecting the various phases of oil prices through the trajectory of market developments. The findings of this paper highlight the significant phases of the series and its relation to real-world phenomena with an indication of early warning signals of future significant events, thereby providing a guide for proper decision making and risk management practices of market participants.

Keywords: crude oil prices; delay vector variance; DVV; nonlinearity; surrogates; wavelet analysis; resilience; risk management.

DOI: 10.1504/IJDSRM.2017.093813

International Journal of Decision Sciences, Risk and Management, 2017 Vol.7 No.4, pp.255 - 280

Received: 24 Feb 2017
Accepted: 30 Jan 2018

Published online: 30 Jul 2018 *

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