Financialization of commodity markets: New evidence from temporal and spatial domains

• Published date: 01 August 2024
• Author: Libo Yin,Hong Cao
• Date: 01 August 2024
• DOI: http://doi.org/10.1002/fut.22514

Received: 18 January 2024

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Accepted: 1 May 2024

DOI: 10.1002/fut.22514

RESEARCH ARTICLE

Financialization of commodity markets: New evidence

from temporal and spatial domains

Libo Yin |Hong Cao

School of Finance, Central University of

Finance and Economics, Beijing, China

Correspondence

Libo Yin, School of Finance, Central

University of Finance and Economics,

Shahe Higher Education Park, Changping

District, Beijing 102206, China.

Email: yinlibowsxbb@126.com

Funding information

The National Natural Science Foundation

of China, Grant/Award Number:

71871234; The Graduate Academic

Exchange Support Program of Central

University of Finance and Economics

Abstract

To address the ongoing contention surrounding the impact of financialization,

this study adopts a ripple‐spreading network model to analyze the transmis-

sion of information across 13 globally significant commodity markets. By

juxtaposing the pre‐and postfinancialization periods, notable disparities in

spillover magnitude are discerned, with overall effects registering at 58% and

85%, respectively. Moreover, the postfinancialization period exhibits acceler-

ated spillover dynamics, necessitating a reduced timeframe (less than 1000

units) in contrast to the prefinancialization period (approximately 2000 units).

Furthermore, a heightened interconnectedness among energy, metal, and

agricultural futures is evident in the postfinancialization period. These

findings furnish compelling evidence regarding the ramifications of financia-

lization on commodity markets.

KEYWORDS

commodities, ripple‐spreading network model, temporal and spacial domains analysis

JEL CLASSIFICATION

C02, F30, G14, G15

1|INTRODUCTION

Commodity financialization denotes the occurrence wherein commodity prices deviate significantly from their

intrinsic values due to substantial influxes of financial capital (Domanski & Heath, 2007; Xiong, 2014). Over the past

two decades, commencing from the early 2000s when international commodity prices witnessed rapid escalations, a

robust academic discourse has emerged regarding commodity financialization. Some scholars posit that financializa-

tion engenders price bubbles and augments interconnections among commodities (Cheng & Xiong, 2014; Hu et al.,

2020; Kupabado & Kaehler, 2021; Tang & Xiong, 2012). Conversely, other studies advocate the contrary stance,

suggesting that the financialization of commodities exerts only a limited influence (Christoffersen et al., 2019; Kilian &

Lee, 2014; Kilian & Murphy, 2014; Knittel & Pindyck, 2016; Yang et al., 2021).

Amidst the ongoing debate surrounding the ramifications of financialization, two pivotal inquiries naturally

emerge. First, can financialization precipitate heightened interconnectedness in the global commodity market? Second,

to what degree, if any, has financialization fostered closer integration among global commodity markets? The crux of

addressing these queries lies in gauging the extent of this proximity. Instead of relying on conventional measures such

as correlation coefficients, regression analyses, or spillover indices (Büyükşahin & Robe, 2014; Dahl et al., 2020; Jiang

et al., 2020; Ordu et al., 2018; Siklos et al., 2020; Tang & Xiong, 2012; Yang et al., 2021), we employ the Ripple Spreading

Network Model (RSNM) proposed by Hu et al. (2011) to evaluate the proximity of 13 globally significant futures

J Futures Markets. 2024;44:1357–1382. wileyonlinelibrary.com/journal/fut © 2024 Wiley Periodicals LLC.

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markets across both temporal and spatial dimensions. Specifically, employing crude oil as a paradigm,

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we utilize the

RSNM model to scrutinize the dynamic dissemination process of crude oil return information to global futures markets

during pre‐and postfinancialization periods. This methodology confers the advantage of encapsulating proximity

across both temporal and spatial realms, not only for the overarching system (i.e., global futures markets) but also for

each individual commodity within the system. In the temporal dimension, we can monitor alterations in the aggregate

number of interconnected edges over time, facilitating an understanding of spillover magnitude via edge accumulation,

as well as spillover velocity by juxtaposing interconnected edges at equivalent times pre‐and postfinancialization. In

the spatial dimension, we can delineate all information pathways leading to each specific futures market and ascertain

their arrival times, enabling a comparison of the structure of global futures markets.

Our analysis reveals that financialization exerts a pronounced impact on the spillover dynamics, velocity, and

configuration of global futures markets. Specifically, the magnitude of spillover resulting from crude oil return

information escalates from 58% to 85% during the postfinancialization period, precipitating an augmented level of

contagion across each market. Moreover, the pace of spillover accelerates, evidenced by a 50% reduction in the time

required for rapid dissemination. Additionally, the structural composition of futures markets undergoes transforma-

tions, as each market in the postfinancialization period experiences direct influences from crude oil futures, thereby

establishing connections among previously isolated markets. Notably, these findings persist robustly when substituting

the information source from the energy sector (crude oil futures) with metals (gold futures) and agricultural products

(corn futures).

The contributions of this paper are manifold. First, we undertake a comprehensive analysis of information

dissemination from commodities to global futures markets, accounting for both temporal and spatial dimensions. In

contrast to prior network methodologies such as those employed by Diebold and Yilmaz (2009,2012,2014), or those

integrated with the approach of Baruník and Křehlík (2018) (Dahl et al., 2020; Jiang et al., 2020; Ouyang et al., 2021;

Siklos et al., 2020; Yang et al., 2021), we adopt and extend the RSNM originally proposed by Hu et al. (2011). This

approach enables us to capture the dynamic process of information dissemination and propagation within global

futures markets. As a result, we are able to quantify spillover scale, which denotes the eventual number of global

futures markets impacted by return information, and spillover velocity, gauged through the comparison of connected

edges between the pre‐and postfinancialization periods. Moreover, for each futures market, we can delineate the

pathway of information transmission, discerning whether it originates directly from the information source or

indirectly through other futures markets. Additionally, we can pinpoint the timing of information arrival at every node

market along the transmission pathway.

Furthermore, we delve into the interplay among diverse commodities. Previous research has predominantly focused

on singular commodity futures markets (Yin & Han, 2013), multiple commodity futures markets within specific

categories such as energy, metals, and agriculture (Hernandez et al., 2014; Jiang et al., 2020), or the relationship

between commodity markets and uncertainty (Li et al., 2016; Yin & Han, 2014). While some recent studies have aimed

to encompass various commodity futures categories, they often concentrate on the interconnections between different

categories or between individual futures within distinct categories (Dahl et al., 2020; Han et al., 2015; Kang et al., 2017).

Our study spans 13 futures markets across three categories (energy, metals, and agricultural products). Hence, we not

only analyze the interactions between different categories but also investigate the interaction between any two futures

markets.

Second, this study contributes to the ongoing discourse regarding the impact of commodity financialization.

Through an examination of information transmission, we offer fresh insights into the influence of financialization on

both the overarching commodities system and each individual commodity, scrutinizing temporal and spatial

dimensions. Collectively, our findings reveal an augmentation in spillover magnitude and velocity following

financialization, accompanied by a more tightly integrated structure of global commodity markets. This underscores

the pivotal role of financialization in shaping the transmission dynamics of futures markets. For each specific

commodity, we observe a reduction in the time taken for return information to reach, increased interconnections

across energy, metal, and agricultural product categories, and alterations in the relationships between specific

commodities. Notably, the previously isolated sugar (SUG) market, devoid of contagion before financialization,

becomes linked to multiple markets postfinancialization. These findings illuminate the significant role played by

financialization in driving the transmission dynamics of commodity markets.

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Given its quintessential representation among various commodities (Diebold et al., 2017).

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YIN and CAO