Risk implications of dependence in the commodities: A copula-based analysis

Prachi, Jain; Maitra, Debasish

doi:10.1016/j.gfj.2023.100859

Cited by 3 publications

(2 citation statements)

References 72 publications

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“…In the fourth step, we analyze risk spillovers using the CoVaR measure based on estimated marginal distributions and t-copulas of returns series to examine Hypothesis 3. The combination of these three models is commonly utilized in the academic literature, as demonstrated by the works of various researchers such as Dai et al (2023), Hanif et al (2022), Jain and Maitra (2023), Kielmann et al (2022), Rehman et al (2023), Yao andLi (2023), andZeng et al (2022), underscoring its validity and widespread acceptance in scholarly discourse. For clarity and easy reference, we have synthesized a summary of their models' application in Table A1.…”

Section: Methodsmentioning

confidence: 99%

“…To address this limitation, Bedford and Cooke (2002) introduced the innovative multivariate copula method known as vine copula. In research employing vine copula approaches, comparison often involves three prevalent structures within the vine copula model: Regular (R-vine), Canonical (C-vine), and Drawable (D-vine) structures (Aslam et al 2023;BenSaïda 2023;Czado 2019;Jain and Maitra 2023). The prevailing assumption in this regard favors the superior performance of R-vine, given its greater flexibility.…”

Section: Dependence Structure Of Sectoral Marketsmentioning

confidence: 99%

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Can ESG Integration Enhance the Stability of Disruptive Technology Stock Investments? Evidence from Copula-Based Approaches

Yu,

Xu,

Chen

2024

JRFM

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This paper provides an investigation into the dependence structure among different disruptive technology sectors driving the Fourth Industrial Revolution and scrutinizes the impact of ESG integration on shaping investments in different tech stock sectors in the presence of ESG consideration, represented by the ESG stock index, versus without specific ESG consideration, represented by the general stock index. The results show that (i) C-vine outperforms R-vine and D-vine when modeling the dependence structure of tech sectors. Intelligent infrastructure is the most crucial sector, with substantial reliance on smart transportation and advanced manufacturing. (ii) ESG integration reduces dependence, especially tail dependence, between tech sectors and the stock market, which benefits the future security sector the most and future communication the least. (iii) ESG integration mitigates risk spillover between tech sectors and the stock market, particularly benefiting final frontiers and intelligent infrastructure. The decrease in downside spillover is more significant compared to upside scenarios. For downside risk, spillover from tech sectors to stock indices is more reduced than the reverse, while the opposite holds for upside risk. These sectoral findings offer insights for market participants in financial market investments, financial regulators in risk management, and listed companies in ESG disclosure.

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