Multi-scale systemic risk and spillover networks of commodity markets in the bullish and bearish regimes

Yang

Journal of Futures Markets

et al. 2023

Self Cite

This paper proposes a new network topology approach to identify the contemporaneous and noncontemporaneous idiosyncratic spillovers of lowermoment and higher-moment risks in commodity futures markets using highfrequency data. Our results show that contemporaneous information has more explanatory power in constructing a network than noncontemporaneous information, especially for higher-moment risk spillover networks. In contemporaneous spillover networks, the role of one commodity future and the structure of the networks vary across different realized estimators. Specifically, gold, silver, and wheat are the main volatility and kurtosis risk transmitters, while corn and silver are the main skewness spillover transmitters. Agricultural futures markets are relatively closed in the volatility and kurtosis risk spillover networks, while in the skewness network, they become closer to precious metal futures. Furthermore, crisis events can enlarge the idiosyncratic volatility spillovers in commodity markets. The total spillover effects of higher-moment risk are stronger than those of lower-moment risk.

Section: Resultscontrasting

confidence: 99%

Section: Noncontemporaneous Idiosyncratic Information Spillover Networkcontrasting

confidence: 90%

“…Agricultural commodity futures act as significant transmitters in kurtosis spillover networks. These findings differ from previous studies (Diebold et al, 2017;Ji et al, 2018;X. Zhang, Yang, et al, 2022) that identified energy commodities as the main risk Note: The abbreviations in the table represent gold, silver, copper, platinum, palladium, natural gas, crude oil, gasoline, soybeans, wheat, and corn, respectively.…”

Section: Noncontemporaneous Idiosyncratic Information Spillover Networkmentioning

confidence: 99%

Section: Noncontemporaneous Network Topology Approachesmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

See 3 more Smart Citations

Contemporaneous and noncontemporaneous idiosyncratic risk spillovers in commodity futures markets: A novel network topology approach

Yang

Journal of Futures Markets

et al. 2023

Self Cite

Journal of Futures Markets

High–low volatility spillover network between economic policy uncertainty and commodity futures markets

Xiang,

Borjigin

2024

Based on the formation and evolution of systemic risk, we study the high‐low volatility spillovers between economic policy uncertainty (EPU) and commodity futures and identify the source of risk accumulation and risk outbreak, as well as the corresponding contagion mechanisms. Upon comparing topological characteristics on each volatility layer, our results demonstrate that high and low volatility spillover networks have different network characteristics and evolution behaviors. At the system level, high volatility spillovers are relatively stronger than spillovers in in low volatility network, while the risk propagation efficiency in the low volatility network is higher. At the market level, EPU is not only an important risk‐emitter but also a risk‐recipient most of the time. Additionally, compared with high volatility network, low volatility network characteristics have greater predictive ability for risk spillover among commodity futures, which means that it contains additional information and provides early warning signals for financial stress.

COVID‐19 and insurance industry: Initial impact of the pandemic based on time‐frequency methods

Fareed,

Iqbal,

Zhang

et al. 2024

Int. J Fin Econ

This study uses a wavelet‐based framework to investigate the co‐movement nexus between COVID‐19 and insurance industry returns in emerging and developed markets. Analysis of the daily observations from 22 January 2020 to 14 September 2020 reveals that insurance returns responded strongly and negatively right after the onset of the global COVID‐19 outbreak but asymmetrically later. Additionally, the devastation brought to the insurance industry is comparatively more severe but short‐lived for emerging markets. The wavelet‐based Granger causality and correlation confirm the robustness of our results. Important implications for investors, industry managers, and policymakers are provided in light of the findings in the aftermath of COVID‐19.