Measuring the Time-Frequency Dynamics of Return and Volatility Connectedness in Global Crude Oil Markets

Yuki Toyoshima,Shigeyuki Hamori

doi:10.3390/en11112893

Abstract

This study analyzes return and volatility spillovers across global crude oil markets for 1 January 1991 to 27 April 2018, using an empirical technique from the time and frequency domains, and makes four key contributions. First, the spillover tables reveal that the West Texas Intermediate (WTI) futures market, which is a common indicator of crude oil indices, contributes the least to both return and volatility spillovers. Second, the results also show that the long-term factor contributes the most to returns spillover, while the short-term factor contributes the most in terms of volatility. Third, the rolling analyses show that the time-variate connectedness in terms of returns tends to be strong, but there was no noticeable change from 1991 to April 2018 in terms of volatility. Finally, the major events between 1991 and April 2018, namely the Asian currency crisis (1997–1998) and the global financial crisis (2007–2008), caused a rise in the total connectedness of returns and volatility.

Highlights

The purpose of this paper is to analyze volatility spillover across global crude oil markets using an empirical technique for the time and frequency domains
Following Diebold and Yilmaz [3,30,31] and Barunik and Krehlik [33], we first estimate a five-variable vector autoregression (VAR) model based on the lag-length selection using the Schwarz–Bayesian information criterion (SBIC) developed by Schwarz [41]
It is surprising that the West Texas Intermediate (WTI) futures market, which is a common indicator of crude oil indices, contributes the least to both returns and volatility spillovers

Summary

Introduction

The purpose of this paper is to analyze volatility spillover across global crude oil markets using an empirical technique for the time and frequency domains. Since West Texas Intermediate Crude Oil (WTI) was listed on the New York Mercantile Exchange (NYMEX) in 1983, the market has formed crude oil prices and has been affected by various events. Since 2014, the economic slowdown in emerging countries has strengthened, and crude oil prices have dropped due to the expected stagnation in demand. In recent years, both economic events and other events, such as OPEC’s production policy, the shale gas boom in the US, and natural disasters such as hurricanes, have influenced crude oil prices. Capturing the volatility spillover of crude oil prices is very useful, for government authorities, and for financial and nonfinancial firms. Ross [1] indicates that volatility provides helpful data on the flow of information

Objectives

Results

Conclusion