Estimating the connectedness of commodity futures using a network approach

Xiao, Binqing; H, Yu; Fang, Libing; Ding, Sifang

doi:10.1002/fut.22086

Cited by 49 publications

(37 citation statements)

References 41 publications

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“…The study by Sanders et al (2004) investigated the financialisation of energy commodities (oil, gas), while metals have been studied relatively more recently by ; H. Mayer et al (2017); J. Mayer (2012); Xiao et al (2019). One of the earliest empirical studies investigating the link between commodity futures trading and spot price volatility is Antoniou & Foster (1992).…”

Section: Literature Reviewmentioning

confidence: 99%

Financialisation of natural resources & instability caused by risk transfer in commodity markets

2020

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Understanding the connectedness of financial markets and hence possible sources of systematic risk is central to the debate on the process of financialisation and its consequences for financial stability. In this study, we examine the connectedness between commodity spot and futures prices by applying a novel frequency connectedness framework on data from January 1979 to December 2019 to measure the connectedness among financial variables. Focusing on the seven most widely traded commodities, including gold, silver, crude oil (WTI and BRENT), corn, soya and iron, we find that (i) volatility of the commodity derivatives (futures) contribute to the spot volatility and hence influence spot prices of the underlying commodities in international markets (ii) volatility spillover effects are stronger in the first four days of the shock, suggesting that shocks to the underlying asset volatility caused by its own fundamental are more prevalent and persistent in the long-term (iii) commodities futures volatility transmission is higher than spot price volatility transmission to the futures prices. Our findings shed new light on the relationship between the actual spot price of commodities and their derivatives and have crucial socio-economic implications in terms of financialisation of important commodities.

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Section: Literature Reviewmentioning

confidence: 99%

Financialisation of natural resources & instability caused by risk transfer in commodity markets

2020

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“…To the best of our knowledge, this is also the first attempt to investigate the determinants of volatility spillovers in commodity futures markets at different frequencies or time horizons. Previous studies (e.g., Diebold et al, 2017;Xiao et al, 2020) that comprehensively examine commodity futures volatility spillovers do not address these issues. Nevertheless, it is documented that there are low frequency (long-term) and high frequency (short-term) components of agricultural commodity price volatility, and their determinants are different (Karali & Power, 2013).…”

Section: Introductionmentioning

confidence: 98%

“…A growing body of literature uses the network approach of Diebold and Yilmaz (2009 to explore return or volatility spillovers in certain categories of commodity futures markets (Dahl et al, 2020;Jiang et al, 2020;Siklos et al, 2020). However, to the best of our knowledge, only a few studies (e.g., Diebold et al, 2017;Xiao et al, 2020) attempt to comprehensively investigate the commodity futures volatility spillover network. Furthermore, these studies provide mixed findings.…”

Section: Introductionmentioning

confidence: 99%

Volatility spillovers in commodity futures markets: A network approach

Yang

Miao

2021

Journal of Futures Markets

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This study examines the volatility connectedness of commodity futures markets by decomposing connectedness at high, medium, and low frequencies using a LASSO-VAR model. The total volatility connectedness across commodities is substantial and fluctuates within the range of 30%-60%, which is largely driven by low-frequency volatility connectedness. Energy futures as a group and crude oil and soybeans as individual commodities play dominant roles as net senders of volatility shocks in the system of 25 commodities.

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“…(2019) and Xiao et al . (2020). Overall spillover indexes estimated by the frequency connectedness approach on different frequency bands fluctuated between 10% and 70%, and they gradually surged between mid‐2008 and late‐2008, sharing a common trend.…”

Section: Resultsmentioning

confidence: 99%

“…(2019), and Xiao et al . (2020). Besides, we diagnose the robustness of the estimated connectedness measures by the D‐Y and the frequency connectedness approaches.…”

Section: Resultsmentioning

confidence: 99%

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

Polat

2020

OPEC Energy Review

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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.

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Estimating the connectedness of commodity futures using a network approach

Cited by 49 publications

References 41 publications

Financialisation of natural resources & instability caused by risk transfer in commodity markets

Financialisation of natural resources & instability caused by risk transfer in commodity markets

Volatility spillovers in commodity futures markets: A network approach

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

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