Oil hedging with a multivariate semiparametric value-at-risk portfolio

Živkov, Dejan; Manić, Slavica; Đurašković, Jasmina; Gajić‐Glamočlija, Marina

doi:10.1016/j.bir.2022.08.004

Cited by 3 publications

(2 citation statements)

References 42 publications

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“…They suggest that certain futures contracts, like the US E-Mini DJIA$5, can help protect against potential UK market losses and avoid interconnectedness. Živkov et al. (2022) explored the oil market’s effect on stock indexes, emphasizing the need for unique strategies to minimize oil-related risks.…”

Section: Background Analysismentioning

confidence: 99%

The degree of Asian-European markets connectedness: examining the impact of global disorders using a spectral analysis

Dimitriou,

Goulas,

Kallandranis

et al. 2024

JABS

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Purpose This paper aims to examine potential diversification benefits between Eurozone (i.e. EURO STOXX 50) and key Asia markets: HSI (Hong Kong), KOSPI (South Korea), NIKKEI 225 (Japan) and TSEC (Taiwan). The sample covers the period from 04-01-2008 to 19-10-2023 in daily frequency. Design/methodology/approach The empirical investigation is based on the wavelet coherence analysis, which is a localized correlation coefficient in the time and frequency domain. Findings The results provide evidence that long-term diversification benefits exist between EURO STOXX and NIKKEI, EURO STOXX and KOSPI (after 2015) and there are signs for the pair and EURO STOXX-TSEC (after 2014). During the short term, there are signs of diversification benefits during the sample period. However, during the medium term, the diversification benefits seem to diminish. Originality/value These results have crucial implications for investors regarding the benefits of international portfolio diversification.

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Section: Background Analysismentioning

confidence: 99%

The degree of Asian-European markets connectedness: examining the impact of global disorders using a spectral analysis

Dimitriou,

Goulas,

Kallandranis

et al. 2024

JABS

View full text Add to dashboard Cite

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“…Empirical analysis of the hedging ability of different assets is not new, for example, precious metals have been considered (Yaya, Tumala, & Udomboso, 2016; Kumar, 2017; Salisu, Vo, & Lawal, 2021), as well as agricultural commodities (Hernandez, Shahzad, Uddin, & Kang, 2018) and stocks (Lin, Zhou, Jiang, & Ou, 2021; Živkov, Manić, Đurašković, & Gajić-Glamočlija, 2022). Hedging roles of some assets have been conducted against oil shocks due to the latter’s high volatility (Maitra, Guhathakurta, & Kang, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

Hedging potentials of green investments against climate and oil market risks

Adediran¹,

Swaray

Orekoya³

et al. 2023

FREP

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PurposeThis study aims to examine the ability of clean energy stocks to provide cover for investors against market risks related to climate change and disturbances in the oil market.Design/methodology/approachThe study adopts the feasible quasi generalized least squares technique to estimate a predictive model based on Westerlund and Narayan’s (2015) approach to evaluating the hedging effectiveness of clean energy stocks. The out-of-sample forecast evaluations of the oil risk-based and climate risk-based clean energy predictive models are explored using Clark and West’s model (2007) and a modified Diebold & Mariano forecast evaluation test for nested and non-nested models, respectively.FindingsThe study finds ample evidence that clean energy stocks may hedge against oil market risks. This result is robust to alternative measures of oil risk and holds when applied to data from the COVID-19 pandemic. In contrast, the hedging effectiveness of clean energy against climate risks is limited to 4 of the 6 clean energy indices and restricted to climate risk measured with climate policy uncertainty.Originality/valueThe study contributes to the literature by providing extensive analysis of hedging effectiveness of several clean energy indices (global, the United States (US), Europe and Asia) and sectoral clean energy indices (solar and wind) against oil market and climate risks using various measures of oil risk (WTI (West Texas intermediate) and Brent volatility) and climate risk (climate policy uncertainty and energy and environmental regulation) as predictors. It also conducts forecast evaluations of the clean energy predictive models for nested and non-nested models.

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