Revisiting the relationship between spot and futures markets: evidence from commodity markets and NARDL framework

Ameur, Hachmi Ben; Ftiti, Zied; Louhichi, Waël

doi:10.1007/s10479-021-04172-3

Cited by 23 publications

(10 citation statements)

References 32 publications

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“…Literature shows that during COVID-19 the markets are more sensitive to bad news rather than positive news (see Ftiti, Louhichi, & Ben Ameur, 2021 ). Thus, aligned with Ameur et al (2021) , we utilize a Shin et al (2014) nonlinear autoregressive distributed lag (NARDL) framework because it takes into account asymmetry and nonlinearity in both the long- and short-term scenarios. To estimate NARDL in a comparison of the US and China we analysed the Shin et al (2014) model.…”

Section: Resultsmentioning

confidence: 99%

Corporate vulnerability in the US and China during COVID-19: A machine learning approach

Khan

Segovia

Bhatti

et al. 2023

The Journal of Economic Asymmetries

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Section: Resultsmentioning

confidence: 99%

Corporate vulnerability in the US and China during COVID-19: A machine learning approach

Khan

Segovia

Bhatti

et al. 2023

The Journal of Economic Asymmetries

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“…Some recent studies argue that the association between two indicators could be nonlinear and that there is an optimal threshold that affects such relations. Several studies have verified this nonlinear relationship; for example, Ameur et al ( 2022 ) for the financial market sector, Hakimi and Hamdi ( 2019 ) for the financial development and human development relationship, and Boussaada et al ( 2022 ) and Hakimi et al ( 2020 ) for the banking sector. Recently, Ameur et al ( 2022 ) studied the relationship between the spot and futures markets using the NARDL model.…”

Section: Literature Reviewmentioning

confidence: 90%

“…Several studies have verified this nonlinear relationship; for example, Ameur et al ( 2022 ) for the financial market sector, Hakimi and Hamdi ( 2019 ) for the financial development and human development relationship, and Boussaada et al ( 2022 ) and Hakimi et al ( 2020 ) for the banking sector. Recently, Ameur et al ( 2022 ) studied the relationship between the spot and futures markets using the NARDL model. The findings support a bidirectional relationship between both markets over the short- and long-run with a greater lead for the futures market.…”

Section: Literature Reviewmentioning

confidence: 90%

Dirty versus renewable energy consumption in China: a comparative analysis between conventional and non-conventional approaches

et al. 2023

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This study uses two empirical approaches to explore the asymmetric effects of oil and coal prices on renewable energy consumption (REC) in China from 1970 to 2019. As a conventional approach, we used the nonlinear autoregressive distributed lags (NARDL) model, while machine learning was used as a non-conventional approach. The empirical findings of the NARDL indicate that oil and coal price fluctuations have a significant effect on REC for both the short and long term. The results of the non-conventional approaches based on machine learning indicated that the SVM model was more efficient than the KNN model in terms of accuracy, performance, and convergence. Referring to the SVM model findings, the results show that an increase in the coal price has a higher ability to predict REC than the oil price. As a robustness check, we also find that an increase in Brent prices significantly decreases REC. The findings of this study support the view that there is a substitution effect from oil to coal before initiating the use of renewable energy in China.

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“…Ameur et al, 2020). Another strand of research considers spillover between commodity markets (Ameur et al, 2021a), spatial contagion (Miled et al, 2022), spot and futures markets (Ameur et al, 2021b), jumps (Jawadi et al, 2019) and hedging analysis (Madani & Ftiti, 2022). 3 Although the existing literature provides evidence of asymmetric connectedness between upper tails and lower tails (e.g.…”

Section: Orcidmentioning

confidence: 99%

Volatility spillovers during normal and high volatility states and their driving factors: A cross‐country and cross‐asset analysis

et al. 2022

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Understanding the transmission of volatility across markets is essential for managing risk and financial stability, especially under crisis periods during which an extreme event occurring in one market is easily transmitted to another market. To gain such an understanding and enrich the related literature, we examine in this article the system of volatility spillovers across various equity markets and asset classes using a quantile‐based approach, allowing us to capture spillovers under normal and high volatility states. The sample period is 16 March 2011–10 November 2020 and the employed dataset comprises 12 implied volatility indices representing a forward‐looking measure of uncertainty of global equities, strategic commodities and the US Treasury bond market. The results show that the identity of transmitters and receivers of volatility shocks differ between normal and high volatility states. The US stock market is at the centre of volatility spillovers in the normal volatility state. European and Chinese stock markets and strategic commodities (e.g. crude oil and gold) become major volatility transmitters in the high volatility state, after acting as volatility receivers during normal periods. Furthermore, we study the drivers of implied volatility spillovers using regression models and find that US Default spread contributes to the total volatility spillover index in both volatility states, whereas TED spread plays a significant role in the normal volatility state. As for the role of short rate and risk aversion, it is significant in the high volatility state. These findings matter to the decision‐making process of risk managers and policymakers.

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Revisiting the relationship between spot and futures markets: evidence from commodity markets and NARDL framework

Cited by 23 publications

References 32 publications

Corporate vulnerability in the US and China during COVID-19: A machine learning approach

Corporate vulnerability in the US and China during COVID-19: A machine learning approach

Dirty versus renewable energy consumption in China: a comparative analysis between conventional and non-conventional approaches

Volatility spillovers during normal and high volatility states and their driving factors: A cross‐country and cross‐asset analysis

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