Cross herding between American industries and the oil market

BenMabrouk, Houda; Litimi, Houda

doi:10.1016/j.najef.2018.02.009

Cited by 36 publications

(23 citation statements)

References 56 publications

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“…The herding tests for Model I are presented in Table 2 , where the estimated

are positive and statistically significant in all renewable and fossil fuel energy markets, which suggest non-herding behaviour in energy markets. Not surprising, these empirical results are consistent with previous studies for testing the overall energy stock market (see, for example [ 18 ], Litimi et al (2016) [ 20 ], BenMabrouk and Litimi (2018), and [ 47 ] Trueck and Yu (2018)).…”

Section: Resultssupporting

confidence: 91%

“…The empirical results are similar to those in a recent study by Ref. [ 20 ] BenMabrouk and Litimi (2018), who found herding behaviour in energy markets during extreme downward movements in oil prices.…”

Section: Resultssupporting

confidence: 90%

“…Subsequent studies have confirmed the findings in Ref. [ 18 ] Litimi et al (2016), and also found herding behaviour was frequently present during the GFC and periods of bubbles ([ 19 ] BenSaïda, 2017 [ 20 ]; BenMabrouk and Litimi, 2018)).…”

Section: Literature Reviewsupporting

confidence: 65%

See 2 more Smart Citations

Herding behaviour in energy stock markets during the Global Financial Crisis, SARS, and ongoing COVID-19*

Chang

McAleer

Wang

2020

Renewable and Sustainable Energy Reviews

144

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Environmental change created worldwide interest in investing in renewable energy. Less reliance on fossil fuels would have a substantial influence on investors for alternative energy, especially renewable energy. The literature has concentrated on empirical studies of herding behaviour in finance, but not in renewable energy. This paper fills the gap by investigating herding in renewable energy, using daily closing prices in renewable and fossil fuel energy stock returns in the USA, Europe, and Asia, for March 24, 2000–May 29, 2020, which covers the Global Financial Crisis (GFC) (2007–2009), the coronavirus crises of SARS (2003). And the ongoing COVID-19 (2019–2020) pandemic. The paper shows that: (1) for low extreme oil returns, investors are more likely to display herding in the stock market; (2) for SARS and COVID-19, herding is more likely during extremely high oil returns after the GFC; and (3) herding is more likely during periods of extremely low oil returns during the coronavirus crises. These results suggest that after the GFC, investors are more sensitive to asset losses, so they will be more likely to display herding in the stock market. However, during SARS and COVID-19, investors panic so they may unwisely sell their assets. There are strong cross-sector herding spillover effects from US fossil fuel energy to renewable energy, especially before the GFC, while the US fossil fuel energy market has a significant influence on the Europe and Asia renewable energy returns during COVID-19. During SARS, which was not a pandemic, US fossil fuels only had an impact on US renewable energy returns.

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“…The herding tests for Model I are presented in Table 2 , where the estimated

Section: Resultssupporting

confidence: 91%

Section: Resultssupporting

confidence: 90%

See 1 more Smart Citation

Herding behaviour in energy stock markets during the Global Financial Crisis, SARS, and ongoing COVID-19*

Chang

McAleer

Wang

2020

Renewable and Sustainable Energy Reviews

144

View full text Add to dashboard Cite

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“…From their perspective, BenMabrouk and Litimi (2018) study herding behaviour at US industries during extreme oil market movements. By employing a modified version of the cross-section absolute deviation methodology, evidence is provided that no herding takes place in any sector.…”

Section: Studies About Herding Phenomena In Traditional Financial Assmentioning

confidence: 99%

“…Herding has made its appearance in a wide spectrum of alternative financial assets through time and has attracted early attention by high-quality academic studies ( Nofsinger and Sias, 1999 ). To be more precise, herding phenomena have been studied concerning stock markets ( Chang et al., 2000 ; Chiang and Zheng, 2010 ; Balcilar et al., 2014 ; Litimi et al., 2016 ; Bohl et al, 2017 ), commodities markets ( Babalos and Stavroyiannis, 2015 ; BenMabrouk and Litimi, 2018 ), bond markets ( Galariotis et al., 2016 ) and mutual funds ( Deng et al., 2018 ). Furthermore, academic work about herding has focused on the house market ( Ngene et al., 2017 ) and REITs ( Philippas et al., 2013 ).…”

Section: Introductionmentioning

confidence: 99%

Herding behaviour in digital currency markets: An integrated survey and empirical estimation

Kyriazis

2020

Heliyon

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This paper reviews the empirical literature on the highly popular phenomenon of herding behaviour in the markets of digital currencies. Furthermore, a comparison takes place with outcomes from earlier studies about traditional financial assets. Moreover, we empirically investigate herding behaviour of 240 cryptocurrencies during bull and bear markets. The present survey suggests that empirical findings about whether herding phenomena have made a significant appearance or not in cryptocurrency markets are split. The Cross-sectional absolute deviations (CSAD) and Cross-sectional standard deviations (CSSD) approaches for measuring herding tendencies are found to be the most popular. Different behaviour is detected in bull periods compared to bear markets. Nevertheless, evidence from primary studies indicates that herding is stronger during extreme situations rather than in normal conditions. However, our empirical estimations reveal that herding behaviour is evident only in bull markets. These findings cast light on and provide a roadmap for investment decisions with modern forms of liquidity.

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Tail dependence between oil prices and China's A‐shares: Evidence from firm‐level data

Fang

Egan

2020

Int. J Fin Econ

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Research in the area of tail dependence between oil prices and the stock market is sparse, particularly at the firm level. This article investigates lower and upper tail dependences between the price of crude oil and China's A‐share market by estimating an empirical copula with a rolling window. Our results show that tail dependence is increasing over time and that there are differences between lower and upper tail dependences in terms of incremental magnitude. We also find that the impulse responses of tail dependences to shocks to variables of interests vary significantly over the sample period. Our results also indicate that lower tail dependence, in particular, is found to have more than one breakpoint, and the break dates are highly associated with financial crises. In addition, we find evidence of asymmetry in tail dependence, which varies across periods. Finally, we find that tail dependence is persistent in the short‐term but deteriorates as the duration increases. These findings have important implications for investors, risk managers and policy makers.

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Cross herding between American industries and the oil market

Cited by 36 publications

References 56 publications

Herding behaviour in energy stock markets during the Global Financial Crisis, SARS, and ongoing COVID-19*

Herding behaviour in energy stock markets during the Global Financial Crisis, SARS, and ongoing COVID-19*

Herding behaviour in digital currency markets: An integrated survey and empirical estimation

Tail dependence between oil prices and China's A‐shares: Evidence from firm‐level data

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