How do crude oil futures hedge crude oil spot risk after the COVID-19 outbreak? A wavelet denoising-GARCHSK-SJC Copula hedge ratio estimation method

Zhu, Pengfei; Lu, Tuantuan; Chen, Shenglan

doi:10.1016/j.physa.2022.128217

Cited by 9 publications

(3 citation statements)

References 61 publications

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“…The above conclusions suggest that the Clayton quantile regression model can not only depict the average hedge ratio like the mean regression, but also show the ratio in bearish and bullish. Furthermore, the hedge ratio in bearish is lower than that in bullish, just as the conclusion made by Zhu et al (2022), who only employ the wavelet denoising‐GARCHSK‐SJC copula method to estimate the hedge ratio after an extreme event, proving the nonlinearity of dependence and the heterogeneity of market structure again.…”

Section: Empirical Results and Analysissupporting

confidence: 61%

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Hedging performance analysis of energy markets: Evidence from copula quantile regression

Ren,

2023

Journal of Futures Markets

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This study investigates hedging performance with respect to different market structures for energy‐related commodities, including West Texas Intermediate crude oil, Brent crude oil, Chinese crude oil, and Heating oil. Copula quantile regression functions and the generalized autoregressive conditionally heteroscedasticity model are combined to analyze the nonlinear impact of dependence and the heterogeneous impact of market structure changes on hedging performance. Results show that hedging performance presents nonlinearity and market structure changes have surprisingly strong heterogeneous effects on the quantile hedge ratio, where bearish and bullish have lower hedge ratios than normal markets, which is captured better by Clayton copula quantile regression. Additionally, the trend of hedging effectiveness over different market structures also shows an inverted U shape. After changing data frequency or the types of futures contracts, the conclusions remain the same. Our empirical findings imply that hedgers are supposed to adjust the hedging number of futures according to market structure changes to hedge price risk effectively.

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Section: Empirical Results and Analysissupporting

confidence: 61%

“…Internal and external factors, such as energy supply and demand, economic fundamentals, financial crises, major public health affairs, geopolitical conflicts, and so on may lead to extreme fluctuations in energy prices (Zhu et al, 2022). This is to say, energy price risk is everywhere in the real market.…”

Section: Introductionmentioning

confidence: 99%

Hedging performance analysis of energy markets: Evidence from copula quantile regression

Ren,

2023

Journal of Futures Markets

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“…In the long run, the WTI has RAF 23,1 a negative impact on the GBE in the high quantile. One possible explanation is that the outbreak of the pandemic caused the global crude oil price to experience unprecedented extreme fluctuations (Zhu et al, 2022). Market participants no longer actively participate in the green bond market to avoid risks.…”

Section: Role Of Economic and Financial Uncertaintymentioning

confidence: 99%

The heterogeneous role of economic and financial uncertainty in green bond market efficiency

Wei,

Zhou,

Ren

et al. 2023

RAF

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Purpose This paper aims to explore the quantile-specific short- and long-term effects of economic policy uncertainty (EPU) on the efficiency of the green bond market. Design/methodology/approach This study examines the long-term cointegration relationship and the short-term fluctuation relationship of EPU, WTI crude oil price (WTI) and European Union Allowances price (EUA) with the green bond market efficiency (GBE) using the quantile autoregressive distributed lag method. Additionally, the authors analyze the differences before and after the Covid-19 pandemic. Findings EPU has a significant positive impact on the GBE before the outbreak. However, during the crisis period, the impact of EPU and WTI was greatly weakened, whereas the impact of EUA was strengthened. Practical implications This paper demonstrates the dynamics of GBE and its influencing factors under different periods. The findings provide insights for market participants and policymakers to gain a clearer understanding of the green bond market. Originality/value This paper extends the study of green bonds by quantifying the GBE and elucidating the nonlinear relationship between efficiency and independent variables at different quantiles over different periods.

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Behavioural explanations of Expectile VaR forecasting and dynamic hedging strategies for downside risk during the COVID‐19 pandemic: Insights from financial markets

Trichilli,

Gaadane,

Boujelbène Abbes

et al. 2023

Int. J Fin Econ

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In this paper, we investigate the influence of confirmation bias on Expectile Value at Risk (EVaR) forecasting among fundamentalist, optimistic, and pessimistic investors in cryptocurrency, commodity, and stock markets before and during the COVID‐19 pandemic. Utilizing the DCC‐range GARCH model, we also explore the conditional minimum downside risk hedge ratios. Our findings demonstrate that confirmation bias leads to excessive EVaR for financial market returns, regardless of the period being before or during COVID‐19. Moreover, fundamentalists' expectations in all markets remain constant, while without confirmation bias, optimists' and pessimists' expectations tend to converge to zero over time but diverge significantly during turbulent periods. When confirmation bias is present, the average distance between these expectations widens. Analysing the hedge ratio results, with or without confirmation bias, also unveils the conditional minimum downside risk hedge ratios. These ratios indicate the optimal proportions for hedging downside risk in each financial market during different periods. We find that the conditional minimum downside risk hedge ratios are generally lower (higher) during the pre‐COVID‐19 (COVID‐19) period, implying that hedging costs are higher during the COVID‐19 period. These insightful findings offer valuable insights for traders and regulators in identifying and understanding the risk conditions of cryptocurrency, commodity, and stock markets. Additionally, the analysis of conditional minimum downside risk hedge ratios provides investors with essential information on how to strategically position their portfolios to mitigate and manage risk during both tranquil and turbulent market conditions, with and without confirmation bias.

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How do crude oil futures hedge crude oil spot risk after the COVID-19 outbreak? A wavelet denoising-GARCHSK-SJC Copula hedge ratio estimation method

Cited by 9 publications

References 61 publications

Hedging performance analysis of energy markets: Evidence from copula quantile regression

Hedging performance analysis of energy markets: Evidence from copula quantile regression

The heterogeneous role of economic and financial uncertainty in green bond market efficiency

Behavioural explanations of Expectile VaR forecasting and dynamic hedging strategies for downside risk during the COVID‐19 pandemic: Insights from financial markets

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