Emergence of Turbulent Epochs in Oil Prices

TL;DR

This paper introduces a wavelet-based method to detect regime shifts and special epochs in oil prices by analyzing their multi-scale, multi-fractional behavior, revealing events not captured by traditional methods.

Contribution

It develops a novel algorithm for robust detection of regime shifts and multi-fractional dynamics in financial data, enhancing understanding of oil price fluctuations.

Findings

Identification of regime shifts in oil prices using wavelet analysis

Detection of special epochs linked to historical events

Standard methods may miss key regime changes

Abstract

Oil price data have a complicated multi-scale structure that may vary with time. We use time-frequency analysis to identify the main features of these variations and, in particular, the regime shifts. The analysis is based on a wavelet-based decomposition and analysis of the associated scale spectrum. The joint estimation of the local Hurst exponent and volatility is the key to detect and identify regime shifting and switching of the oil price. The framework involves in particular modeling in terms of a process of `multi-fractional' type so that both the roughness and the volatility of the price process may vary with time. Special epochs then emerge as a result of these degrees of freedom, moreover, as a result of the special type of spectral estimator used. These special epochs are discussed and related to historical events. Some of them are not detected by standard analysis based on maximum likelihood estimation. The paper presents a novel algorithm for robust detection of such special epochs and multi-fractional behavior in financial or other types of data. In the financial context insight about such behavior of the asset price is important to evaluate financial contracts involving the asset.