Geopolitical Risk and Extreme Risk Connectedness Among Energy and Other Strategic Commodities: Fresh Sight Using the High‐Dimensional CoVaR Model

• Published date: 01 November 2024
• Author: Qingying Zheng,Jintao Wu,Boqiang Lin
• Date: 01 November 2024
• DOI: http://doi.org/10.1002/fut.22548

Journal of Futures Markets

RESEARCH ARTICLE

Geopolitical Risk and Extreme Risk Connectedness

Among Energy and Other Strategic Commodities: Fresh

Sight Using the High‐Dimensional CoVaR Model

Qingying Zheng

1

| Jintao Wu

1

| Boqiang Lin

2

1

School of Economics, Fujian Normal University, Fuzhou, China |

2

China Institute for Studies in Energy Policy, School of Management, Xiamen University,

Xiamen, China

Correspondence: Boqiang Lin (bqlin@xmu.edu.cn;bqlin2004@vip.sina.com)

Received: 6 February 2024 | Revised: 13 June 2024 | Accepted: 15 July 2024

Funding: This paper is supported by National Natural Science Foundation of China (key Program, No 72133003), the Humanities and Social Science Research

Foundation of Ministry of Education of China (Grant number: 19YJC790199), Natural Science Foundation of Fujian (Grant number: 2023J01512), Research

Foundation of the Academy of Carbon Neutrality of Fujian Normal University (Grant number: TZH2022‐13), “National Finance”and “Mezzo Economics”

teaching and research Fund of Guangfa Securities Social Charity Foundation.

Keywords: geopolitical risk | portfolio management| strategic commodities| tail risk spillovers network

ABSTRACT

Existing studies on commodity market risk spillovers recognize the pivotal role of geopolitical risk (GPR), but scarcely address

how it drives tail risk spillover networks. This study adopts the Tail‐Event driven NETwork methodology to explore high‐

dimensional Conditional Value at Risk (CoVaR) spillovers within energy and other strategic commodity markets. Our findings

indicate that (1) In both lower and upper tail networks, metal and food commodities primarily act as net risk transmitters,

whereas energy commodities are mainly net risk receivers. Additionally, these roles undergo short‐term reversals during

periods of heightened market uncertainty. (2) There exists an asymmetrical pattern of CoVaR co‐movements in these

commodity markets. The total connectedness (TC) in both the upper and lower tails demonstrates distinct responses to various

extreme events. GPR tends to weaken the lower tail TC and strengthen the upper tail. (3) Incorporating GPR substantially

improves the effectiveness of Minimum Connectedness Portfolio (MCoP) for these strategic commodities.

JEL Classification: G10, G11, G19

1 | Introduction

Energy, metals, and food commodities are considered strategic

assets, vital for the stability of a country's economic cycles and

long‐term growth (Zhang, Di, and Farnoosh 2021; Deng, Xu,

and Xing 2023; Polat et al. 2023). A distinctive characteristic of

these commodities is their profound natural interlinkage in

production, consumption, and investment activities (Adeleke,

Awodumi, and Adewuyi 2022). In recent years, this inherent

interconnectedness has further deepened, owing to trends in

financial liberalization, globalization, and technological ad-

vancements (Farid et al. 2022). This development has been

instrumental in reducing information asymmetry and in

augmenting the efficiency of resource allocation (Tiwari

et al. 2022). Nonetheless, such increased interconnectedness

has concurrently led to heightened price volatility and the

amplification of speculative activities (Andreasson et al. 2016;

Naeem et al. 2022), even potentially triggering systemic risk

contagion. Particularly, in the wake of the global financial crisis

(GFC), extreme risk contagion in commodity markets has

garnered the attention of scholars, policymakers, and investors.

Another characteristic of strategic commodities is their

vulnerability to the impacts of geopolitical crises. Recent times

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https://doi.org/10.1002/fut.22548

have unfortunately witnessed an increase in such crises, as

highlighted by events such as the Russo–Ukrainian conflict and

the Israel–Palestine conflict. Amidst such conflicts, the extrac-

tion, production, and supply chains of commodities often face

severe disruption (Gozgor, Khalfaoui, and Yarovaya 2023).

Furthermore, even in scenarios without military engagements,

geopolitical tensions can still significantly disrupt market. This

is often because countries involved in conflicts tend to use

strategic assets as tools against their adversaries (Kilian 2009;

Boute 2022), leading to measures like sanctions, embargoes, and

blockades. In this context, market regulators are keen to gain a

deeper understanding of the patterns of systemic risk contagion

during such crises, whereas investors are increasingly focused

on identifying safe‐haven assets to hedge against extreme risks

and thereby avoid significant losses.

Given this, this study aims to examine the extreme risk spillover

network among energy and other strategic commodities, with a

particular focus on the impact of geopolitical risks (GPRs).

Specifically, we have applied the Tail Event driven NETwork

model (TENET) proposed by Härdle, Wang, and Yu (2016) for

analyzing the high‐dimensional Conditional Value at Risk

(CoVaR) spillovers network. These commodities encompass

energy sources (crude oil, natural gas, and coal), industrial and

precious metals (gold, copper, and aluminum), and food

(wheat, corn, and rice). Notably, the study incorporates the

GPR Index, developed by Caldara and Iacoviello (2022), as a

metric to assess the impact of GPRs. Finally, on the basis of the

Minimum Connectedness Portfolio (MCoP) approach suggested

by Broadstock, Chatziantoniou, and Gabauer (2022), we have

developed a diversification strategy for these strategic

commodities.

Our main findings are as follows: (1) GPR is identified as a

crucial determinant of tail risk spillovers in strategic commodi-

ties. When considering GPR, the total connectedness (TC) tends

to decrease in the lower tail network and increase in the upper

tail network. (2) In both lower and upper tail networks, metals

predominantly function as net transmitters of risk, whereas

energy commodities primarily act as net receivers of risk. (3)

Incorporating GPR significantly enhances the effectiveness of

the MCoP strategy for these strategic commodities.

This article contributes to the existing literature in three

aspects: First, under the consideration of the GPR Index, we

utilize the TENET model to quantify the risk spillover networks

and dynamic TC of strategic commodities. Although earlier

studies have provided valuable insights by examining the

significant impact of GPR on the interconnectedness of

commodity markets—typically through analyses of subsamples

from periods of high GPR (Chishti, Khalid, and Sana 2023; Ding

et al. 2023; Wu et al. 2023) or by using linear models to assess

GPR as determinants of risk interconnectedness (Saeed, Bouri,

and Alsulami 2021; Gong and Xu 2022; Evrim Mandaci, Azimli,

and Mandaci 2023; Le, Pham, and Do 2023)—these approaches

have not directly integrated GPR into the measurement of risk

spillover networks. This oversight potentially hinders effective

tail risk monitoring and portfolio management, because real

risk spillovers among strategic commodities do not occur in

isolation within the economic environment(Härdle, Wang, and

Yu 2016).

Furthermore, this paper extends the aforementioned literature

by providing a risk spillover analysis from an extreme

perspective. Compared to methods based on average quantiles,

this approach aligns more closely with the characteristics of

GPR and the practical demands of strategic commodities

management. Although a few studies have discussed the impact

of GPR on commodity markets from an extreme perspective

(Gong et al. 2023; Nouir and Hamida 2023; Mo, Nie, and

Zhao 2024), they predominantly focus on single‐category

markets and extreme downside risks. Our study expands this

analysis to multi‐category commodity markets, considering

both extreme upside and downside risks. This comprehensive

approach enables market participants to gain a more nuanced

understanding of market dynamics and asymmetries.

Finally, we developed an MCoP for energy and other strategic

commodities and demonstrated that the inclusion of GPR

enhances portfolio performance. Existing studies on multi‐

commodity MCoP predominantly use a time‐varying parameter

(TVP)‐vector auto‐regression (VAR) connectedness approach

(Adekoya et al. 2022; Abdullah, Chowdhury, and Sulong 2023;

Bai et al. 2023; Cagli 2023; Cui and Maghyereh 2023; Pham

et al. 2023; Lou, Xiao, and Lian 2024). Although this method

effectively captures the dynamic nature of interlinked commod-

ity markets, it does not address how integrating additional

variables such as external risks might impact portfolio alloca-

tion and performance. Our methodology, based on the TENET

framework, allows us to compare the impact of GPR on these

aspects. Additionally, this approach provides further multi‐

market evidence that incorporating GPR can improve commod-

ity portfolio performance (Antonakakis et al. 2017; Liu

et al. 2019; Dong et al. 2023).

The remaining structure of this paper includes a literature

review in Section 2. The empirical model is presented in

Section 3, and the data presentation follows in Section 4.

Subsequent sections discuss empirical findings and robustness

checks in Sections 5and 6, respectively, concluding with a

summary in Section 7.

2 | Literature Review

2.1 | Transmission Paths in Commodity Markets

Strategic assets, such as fossil fuels, industrial metals, precious

metals, and major food products, play a vital role in economic

and societal stability. These commodities possess dual char-

acteristics of goods and financial assets, with their prices being

influenced by supply–demand dynamics and market investor

behavior (Noguera‐Santaella 2016; Mitsas, Golitsis, and

Khudoykulov 2022). Additionally, due to their “strategic”

nature, these commodities' prices are particularly sensitive to

non‐fundamental factors, especially GPRs (Cheng, Liao, and

Pan 2023).

The impact of geopolitical uncertainty on commodity prices

involves multidimensional transmission paths. First, the

geographical concentration of energy, mineral resources, and

major food production regions means that political instability

can readily affect the global supply of strategic commodities

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