The Relations of Oil Price Change with Fear Gauges in Global Political and Economic Environment

Jeng-Bau Lin,Wei Tsai

doi:10.3390/en12152982

Abstract

The oil price time series data can be affected by major global political and economic events, which would result in structural changes that could lead to biased estimations. By adopting the Bai and Perron model this paper found that there were six structural breaks in the Brent oil price due to major global events and that ARDL-ECM cointegration exists only between oil price and stock market volatility index (VIX) throughout the sampling period. However, cointegration relations were found between oil price and Crude Oil Volatility Index (OVX) in the second and fourth sub-periods, and also between oil price and VIX in the second, third, fourth, sixth, and seventh sub-periods. Furthermore, the cointegration relation coupled with correlation analysis indicates a long-term equilibrium positive (negative) relation between the two variables. Such relations existed between the price and the two fear gauges, respectively, only for some specific sub-periods, implying that OVX seemed to be better than VIX in predicting oil price changes. We suggest that the investors in the global oil market must pay attention to not only the impacts of major global political and economic events on oil price, but also the positive or negative correlations between oil price and fear gauge.

Highlights

Many major political and economic events such as the overall economic impact [1], geopolitics [2], production capacity [3], supply and demand growth [4], speculative investment [5,6], European debt crisis [7], quantitative easing monetary policy [8,9], US exchange rate volatility [10,11], and crude oil inventory [12] tend to cause dramatic volatility in the prices in the global huge oil market
This paper aims to investigate what caused multiple structural changes, how the structural changes affected the oil price behavior, and what relationships existed between oil price and the two fear gauges (OVX and volatility index (VIX)) of investors, reflecting oil price volatility under multiple structural changes
Following the suggestion proposed by [25], this paper adopted two sequential procedure by [54], and we find that Bayesian information8 criterion (BIC) and LWZ are the minima when the number of structural change points is methods, SupFT(i) and SupFT(l+1|l), to determine the number of structural change points and indicate

Summary

Introduction

Many major political and economic events such as the overall economic impact [1], geopolitics [2], production capacity [3], supply and demand growth [4], speculative investment [5,6], European debt crisis [7], quantitative easing monetary policy [8,9], US exchange rate volatility [10,11], and crude oil inventory [12] tend to cause dramatic volatility in the prices in the global huge oil market These major global political and economic events can lead to structural changes to long-term economic behaviors [2,13,14,15,16,17]. Reference [20] employed the Ordinary Least Square Cumulative Sum (OLS-CUSUM)

Objectives

Methods

Findings

Conclusion