Exploring the resilience of crude oil market via nonlinear dynamics and wavelet-based analysis: an international experience Online publication date: Mon, 06-Aug-2018
by Emmanuel Senyo Fianu
International Journal of Decision Sciences, Risk and Management (IJDSRM), Vol. 7, No. 4, 2017
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.
Online publication date: Mon, 06-Aug-2018
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Decision Sciences, Risk and Management (IJDSRM):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email email@example.com