Crude oil market autocorrelation: Evidence from multiscale quantile regression analysis

Sun, Jie; Zhao, Xiaochuan; Cai, Xu

doi:10.1016/j.eneco.2021.105239

Cited by 11 publications

(1 citation statement)

References 39 publications

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“…Additionally, it is worth noting that the correlation coefficient at extreme quantiles estimated by linear model is even not always lower than those at middle quantiles, such as WTI crude oil and Brent crude oil markets. Therefore, compared with the existing literature (Shrestha et al, 2018;Sun et al, 2021), who employ a linear quantile regression model, our discoveries may be more accurate in describing the nonlinearity and heterogeneity of dependence between returns, especially tail dependence.…”

Section: Estimation Of the Correlation Coefficient And The Hedge Ratiomentioning

confidence: 87%

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: Estimation Of the Correlation Coefficient And The Hedge Ratiomentioning

confidence: 87%

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

Ren,

2023

Journal of Futures Markets

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A novel PM2.5 concentrations probability density prediction model combines the least absolute shrinkage and selection operator with quantile regression

Yang

2022

Environ Sci Pollut Res

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Causal decomposition on multiple time scales: Evidence from stock price-volume time series

Cai

Zhao

Wang

2022

Chaos, Solitons & Fractals

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Crude oil market autocorrelation: Evidence from multiscale quantile regression analysis

Cited by 11 publications

References 39 publications

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

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

A novel PM2.5 concentrations probability density prediction model combines the least absolute shrinkage and selection operator with quantile regression

Causal decomposition on multiple time scales: Evidence from stock price-volume time series

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