Gold and crude oil as safe-haven assets for clean energy stock indices: Blended copulas approach

Bouri, Elie; Jalkh, Naji; Dutta, Anupam; Uddin, Gazi Salah

doi:10.1016/j.energy.2019.04.155

Cited by 179 publications

(63 citation statements)

References 38 publications

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“…There is literature on clean energy equity dynamics that largely focuses on the impact of oil prices on clean energy stock returns (Bondia et al 2016;Dutta 2017;Dutta et al 2018;Elie et al 2019;Gupta 2017;Henriques and Sadorsky 2008;Kumar et al 2012;Maghyereh et al 2019;Managi and Okimoto 2013;Reboredo 2015;Reboredo et al 2017b;Reboredo and Ugolini 2018;Uddin et al 2019;Wen et al 2014). Popular modelling approaches include multifactor models (Henriques and Sadorsky 2008;Gupta 2017;Reboredo et al 2017b;Bohl et al 2013;Sadorsky 2012), vector autoregressions (Kumar et al 2012;Dutta et al 2018), or other approaches like wavelets (Maghyereh et al 2019;Reboredo et al 2017a), copulas (Reboredo 2015), and quantiles (Uddin et al 2019;Dawar et al 2021).…”

Section: Discussionmentioning

confidence: 99%

A Random Forests Approach to Predicting Clean Energy Stock Prices

Sadorsky

2021

JRFM

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Climate change, green consumers, energy security, fossil fuel divestment, and technological innovation are powerful forces shaping an increased interest towards investing in companies that specialize in clean energy. Well informed investors need reliable methods for predicting the stock prices of clean energy companies. While the existing literature on forecasting stock prices shows how difficult it is to predict stock prices, there is evidence that predicting stock price direction is more successful than predicting actual stock prices. This paper uses the machine learning method of random forests to predict the stock price direction of clean energy exchange traded funds. Some well-known technical indicators are used as features. Decision tree bagging and random forests predictions of stock price direction are more accurate than those obtained from logit models. For a 20-day forecast horizon, tree bagging and random forests methods produce accuracy rates of between 85% and 90% while logit models produce accuracy rates of between 55% and 60%. Tree bagging and random forests are easy to understand and estimate and are useful methods for forecasting the stock price direction of clean energy stocks.

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Section: Discussionmentioning

confidence: 99%

A Random Forests Approach to Predicting Clean Energy Stock Prices

Sadorsky

2021

JRFM

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“…, 2018). Crude oil is an important strategic commodity that plays a vital role in fostering industrial modernization (Elie et al. , 2019).…”

Section: Literature Reviewmentioning

confidence: 99%

“…(2019) also explore the dependence structure between oil, gold and the USD exchange rate, and suggest that the dependence among them is significant. In the same vein, Elie et al. (2019) pay attention to the potential roles of gold and crude oil as safe-haven assets against extreme down movements in clean energy stock markets.…”

Section: Literature Reviewmentioning

confidence: 99%

Asymmetric connectedness among S&P 500, crude oil, gold and Bitcoin

Hung

2022

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PurposeThis paper investigates the dynamic intercorrelation among cryptocurrency (Bitcoin) and conventional financial assets (gold, oil and S&P 500).Design/methodology/approachThe dynamic contemporaneous nexus has been analyzed using spillover index developed and extended by Diebold and Yilmaz (2012, 2014) and Kyrtsou-Labys (2006) nonlinear causality tests. This study is implemented using the daily data spanning from January 2013 to December 2021.FindingsFirst, using the spillover index, the authors find evidence that the S&P 500 was a net transmitter of volatility from oil and gold markets, but a net receiver of volatility from Bitcoin. Return spillovers from crude oil were transmitted first to gold, and Bitcoin markets and return spillovers from gold were transmitted to Bitcoin. Second, Kyrtsou-Labys nonlinear causality tests provide us further insights into the lead-lag interconnections among the four key considered variables from the economic perspective. Specifically, a close inspection of these empirical results, the integration of the four key assets is significant. Similarly, price fluctuation dependency among Bitcoin, stock, gold and oil markets is generally minimal, but it strengthens throughout the COVID-19 period.Originality/valueThis paper is the first study employing the spillover index Diebold-Yilmaz alongside with Kyrtsou-Labys nonlinear causality tests not only to capture the directional return spillover effects but also to highlight the potential presence of asymmetric causality relationships, nonlinear effects among assets under investigation that the previous studies have been ignored in these relations. Therefore, the main contribution of this article to the related literature in this field is significant.

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“…Moreover, gold and Crude oil are no more than weak safe haven assets for clean energy indicators. However, the superiority of crude oil to gold is evidenced in the case of infinitely extreme market movements (Elie et al, 2019). Besides, gold and the US dollar can show their significant role as a safe haven for emerging stocks; the US dollar is better than gold in most cases, while its superiority in hedging infinitely extreme risks is weakened in the subsample of the global crisis and the out-of-sample; and that the downside risk gains offered by the US dollars for China and Thailand are very interesting (Wen et al, 2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Study on Gold as a Hedge or Safe Haven for the Stock Market by a Markov Switching Approach

Ariannejad

Tehrani

2021

J. Mon. Ec.

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Although gold is no longer a central cornerstone of the international monetary and financial system, it still attracts considerable attention from researchers and investors. Nowadays, many investors manage their risk with valuable assets such as gold. This paper examines the dynamic relationships between gold and stock markets in the Tehran Stock Exchange. We have applied the Markov switching method to study the role of gold as a hedge or safe haven for the Tehran Stock Exchange risk from 1998 to 2018. The high dependence and low dependence regimes used in the Markov switching model are based on empirical results that show two regimes for all markets under investigation: a low volatility regime and a high volatility regime. The study's findings show that gold can act as a strong hedge and cannot act as a safe haven for risk of The Tehran stock exchange.

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Gold and crude oil as safe-haven assets for clean energy stock indices: Blended copulas approach

Cited by 179 publications

References 38 publications

A Random Forests Approach to Predicting Clean Energy Stock Prices

A Random Forests Approach to Predicting Clean Energy Stock Prices

Asymmetric connectedness among S&P 500, crude oil, gold and Bitcoin

Study on Gold as a Hedge or Safe Haven for the Stock Market by a Markov Switching Approach

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