Measuring dynamic connectedness networks in energy commodities: evidence from the D‐Y and frequency connectedness approaches

• Published date: 01 December 2020
• Author: Onur Polat
• Date: 01 December 2020
• DOI: http://doi.org/10.1111/opec.12188

Measuring dynamic connectedness

networks in energy commodities: evidence

from the D-Y and frequency connectedness

approaches

Onur Polat

Department of Public Finance, Faculty of Economic and Administrative Sciences, Bilecik Seyh Edebali

University, 1st Floor, Gulumbe Campus, Bilecik11210, T¨

urkiye. Email: onur.polat@bilecik.edu.tr

Abstract

In this study, we examine the energy commodities connectedness between the period June 2006

and April 2020 by implementing the Diebold–Yilmaz and the frequency connectedness

approaches. We estimate dynamic connectedness between WTI crude oil, the Henry Hub natural

gas, ULS diesel and the gasoline prices over the analysed period. Overall spillover indexes

estimated by both methodologies properly respond to prominent geopolitical events over the

sample period. Additionally, we plot network graphs for directional spillovers ref‌lecting two

distinct periods, 2007:3–2019:12 and 2020:1–2020:4. Network analysis verif‌ies that the directional

spillovers between energy commodities have prominently surged due to the COVID-19 outbreak.

The f‌indings of the study underline the importance of an effective regulatory framework for

monitoring commodity price developments to avoid adverse effects of commodity price shocks.

Additionally, the authorities should enact policy actions to counteract the detrimental effects of the

COVID-19 pandemic on the commodity markets.

Key words: Commodity Connectedness, Diebold–Yilmaz Connectedness, Frequency

Connectedness, Network Analysis

1. Introduction

In line with the f‌inancialisation of the commodity markets in the recent past, price

developments in the commodity markets can signif‌icantly affect the global economic

system. Therefore, both authorities and researchers have drawn overwhelming attention

to commodity market developments

1

. Commodities play an essential role in the primary

and the secondary sectors in the economy, and accordingly, a structural price shock

stemming from the commodity market may trigger business cycle f‌luctuations in both

emerging and advanced economies.

JEL classif‌ication: C58, F37, G10.

©2020 Organization of the Petroleum Exporting Countries. Published by John Wiley & Sons Ltd, 9600 Garsington

Road, Oxford OX4 2DQ, UK and 350 Main Street, Malden, MA 02148, USA.

404

Energy markets constitute an important part of the commodity markets, and energy

price shocks can signif‌icantly affect the world economy via various channels such as

derivatives, trade and stock market. Additionally, owing to the strong connectedness

between energy and f‌inancial markets, contagious effects of a f‌inancial shock can rapidly

spread to the energy markets and lead to an increase in energy prices. The 2007–2009

global f‌inancial crisis illustrates an example of this (Zhang and Broadstock, 2018).

Despite featuring different price dynamics than the traditional assets such as stocks,

bonds and fx rates (Kat and Oomen, 2007), f‌inancialisation and globalisation of energy

markets have entailed understanding the dynamics of spillovers between the main energy

commodities and ultimately to detect the dynamic nature of the interconnectednes s

between them.

It is well documented in the f‌inancial contagion literature that the connectedness

between assets surges around f‌inancial distress periods (Baig and Goldfajn, 1999;

Salgado et al., 2000; Karolyi, 2003; Gardini and Angelis, 2012). In a similar vein, it

is expected that the connectedness between commodity markets intensif‌ies around

prominent geopolitical as well as f‌inancial stress events (Diebold et al., 2017; Zhang

and Broadstock, 2018; Yoon et al., 2019; Ji et al., 2020). Departing from the

phenomenon that the connectedness between energy markets being one of the early

warning indicators of economic imbalances, this study aims to measure energy market

connectedness both dynamically and statically in a f‌ixed rolling-sample window. In

this context, this study employs the Diebold–Yilmaz connectedness methodology

proposed by Diebold and Yilmaz (2012) and ‘the frequency connectedness’approach

developed by Barun´

ık and Kˇ

rehl´

ık (2018) by using the daily crude oil, natural gas,

unleaded gasoline and ultra-low sulphur diesel data. In doing so, we estimate energy

commodities connectedness using variance decompositions of the forecast errors of

the VAR model in a 200-day rolling window and a 300-day rolling window on

different frequency bands. Furthermore, network analysis for the commodity

connectedness is conducted for the D-Y and the frequency connectedness method-

ologies over to distinct periods, 2007:3–2019:12 and 2020:1-4, which contains the

COVID-19 outbreak.

The D-Y connectedness approach was f‌irst introduced by Diebold and Yilmaz

(2009) and relied on the forecast error variance decompositions of the VAR model.

However, the connectedness measures estimated by this approach are dependent on the

ordering of variables in the VAR and the framework only estimates total spillovers.

Diebold and Yilmaz (2012) estimate the D-Y connectedness between variables by using

the generalised vector autoregressive framework, where the variance decompositions of

the VAR are order invariant to ordering of the variables. Furthermore, the approach

includes directional spillovers. Consequently, the Diebold and Yilmaz (2012)

©2020 Organization of the Petroleum Exporting Countries OPEC Energy Review December 2020

Measuring Commodity Connectedness Networks 405