Asymmetric effects and volatility transmission from metals markets to solar energy stocks: Evidence from DCC, ADCC, and quantile regression approach (2023)


The emergence of renewable energy technologies and resources is acting as a ray of hope against climatic change and pollutant emissions. Albeit, the clean or low-carbon energy sector grew at a slower rate before the year 2015 (Dutta, 2019, Lee et al., 2023), the wind and solar energy sector made their place in the global market with a growth rate of 15% from the year 2015–2020. Following sustainable development goals (SDGs), the sector is expected to reach up to 45% market share of electricity generation by the year 2030 (Jaeger, 2021). Although wind power (51%) is the leading renewable energy compared to solar energy (26%), the growth rate of solar power consumption may outpace wind energy in near future (Rapier, 2020).

The renewable energy stock market is one of the major sources of financing and development for green technologies and clean energy resources. The development of the stock market support countries to achieve sustainable development and low-carbon economic goals by fostering the renewable energy sector (Lv et al., 2023, Zeqiraj et al., 2020). Besides the environmental impacts of renewable energy sectors, higher oil price volatility is also encouraging investors to capitalize on clean energy stocks (Kumar et al., 2012; Reboredo, 2015). Green financial assets also attract the attention of long-term institutional investors who are committed to decarbonizing the economic sectors and hold these assets for a longer period compared to individual investors (Sangiorgi and Schopohl, 2021). However, the clean energy market follows more speculative behavior compared to traditional energy industries due to their technical characteristics (Bohl et al., 2013) and has momentous interdependence with different factors including the fossil energy market, investor sentiments (Song et al., 2019), reserve currency value, technology stocks, and oil prices (Ahmad, 2017; Kocaarslan and Soytas, 2019, 2021).

Despite the faster growth rate of the solar energy market, it does not receive considerable attention to address its dynamic dependency, asymmetric relationship, and conditional correlations with other variables. Dutta (2019) analyzed the effect of silver volatility on solar energy stocks due to the use of this precious metal (around 20g) in the manufacturing of typical solar panels. Nonetheless, we argue that Silver is not the major component used in solar energy systems. Silicon is the most common semiconductor and heavily used material used in solar panels. Currently, around 95% of the solar modules in the market are made of crystalline silicon cells. These cells form a crystal lattice that efficiently converts light into electricity (Woodhouse et al., 2016). Thus, the silicon market and solar energy stock market are believed to be interdependent up to a considerable extent.

The excess electricity generated by solar panels is generally stored in batteries. Since households and industries are seeking for efficient backup solutions to steer clear of the high-energy cost of their utility supply, solar batteries have become vital components of the off-grid solar energy market (Lehtola and Zahedi, 2019). Two major types of batteries are available in the market, i.e., Lead-acid and Lithium-Ion batteries. Nonetheless, lithium-ion batteries are more in demand due to their significant advantages over other alternatives in terms of efficiency, deep discharge capability, cycle life, and durability (Chen et al., 2020). Therefore, the fluctuation in the lithium market may guide investors in optimally designing their solar energy portfolios.

Besides silicon and lithium, rare earth metals (REMs) such as gallium, terbium, tellurium, selenium, indium, dysprosium, and neodymium are widely used in solar photovoltaics (Hanif et al., 2023; Lee et al., 2022). Hence, a significant volatility spillover exists between the REM and clean energy markets (Zheng et al., 2021). Nevertheless, the supply of important metals employed in solar technologies was largely disrupted by the US-China trade conflicts and COVID-19 global outbreak (Song et al., 2021). Consequently, the transmission of cross-market innovations between the metal and solar energy markets, along with the underlying mechanisms and spillover pattern, is a significant current issue with limited attention in academic literature.

The study aims to investigate the dynamic connectedness, time-varying co-movements, and asymmetric relationship among silicon, lithium, REMs, and solar energy market over the period 2011 to 2022 which covers potential economic events including Paris Agreement, US-China trade frictions, COVID-19 pandemic outbreak, and Russia-Ukraine war. Since portfolio diversification and potential hedging opportunities in green assets are of crucial interest to ethical market participants, our study is motivated to ignite investment in solar energy markets through a better understanding of spillover from underlying metals. The price volatility that emerged from metal supply distortion and trade wars substantially triggered skepticism in clean energy markets (Umar et al., 2022; Wen et al., 2023). The time-varying connectedness and risk spillover can guide investors in reviving their trust in green investment (Dogan et al., 2022).

Our study contributes to the scarce and insufficient literature on the dynamic connectedness and spillover between metal and solar energy markets in four potential ways. First, we introduce silicon and lithium markets which are more sensitive to the development of solar energy systems compared to the previously studied metals, i.e. silver (Dutta, 2019), REMs (Hanif et al., 2023; Zheng et al., 2021), aluminum, cobalt, copper, and nickel (Gustafsson et al., 2022). Secondly, we employed both VAR symmetry dynamic (DCCs) and asymmetric dynamic conditional correlations (ADCCs) which accounts for heavy tails and asymmetric returns. Additionally, the DCC models allow us to estimate time-varying hedge ratios to address the hedging effectiveness of underlying assets. These models can be re-estimated in real-time and provide better estimates of return (risk) compared to traditional models that assume a constant covariance matrix (Singhal and Ghosh, 2016; Lee and Wang, 2022). In order to avoid spurious inferences regarding the existence of financial contagion between asset returns, DCC and ADCC models account for heteroscedasticity bias (Forbes and Rigobon, 2002; Lee and Lee, 2022).

Thirdly, the asymmetric effects of silicon, lithium, and REMs market on solar energy stocks are analyzed after controlling for investor sentiments (VIX) and oil price volatility (OVX). Owing to the linearity assumption of previous conventional models and theories, they remain deficient in guiding financial decisions (Shin et al., 2014; Lee and Hussain, 2023). For a more holistic view of the relationship among underlying variables under bearish, stable, and bullish market conditions, quantile regression is used with the bootstrapped estimate of the entire variance-covariance matrix of the estimators (Gould, 1993, 1998). Since financial time series experience extreme events, quantile regression is robust to such outliers compared to mean regression which is unable to deal with skewed or heavy-tailed distributions. A heterogeneity-consistent approach might help investors to capture heterogeneity in solar energy stock pricing as the asymmetric impacts of commodities and equity prices on the clean energy market vary based on business cycles, oil shocks, geopolitical risks, economic policy uncertainty, and crisis period (Dutta, 2019; Kocaarslan and Soytas, 2019, 2021; Lee et al., 2021; Wang et al., 2023).

Fourthly, we examined the change in the effect of silicon, lithium, and REMs stocks on solar energy stocks subject to sentiment level using threshold regression. Previous literature suggested that clean energy stocks can be hedged by taking a long position on VIX (Ahmad et al., 2018; Dutta, 2019; Liu et al., 2023) as market sentiments reflect the susceptibility of financial series to shocks. Thus, it is imperative to know the sentiment threshold at the disaggregate level for guiding investors' portfolio diversification and short-term cover under the low (high) sentiment level. The significance, magnitude, and direction of asymmetric effects between silicon stocks, lithium stocks, REMs and solar energy market will provide options to diversify investors’ portfolios by including other tradeable assets that consistently deliver reliable and steady returns.

The rest of the paper is divided into seven sections. Section 2 is related to data and methods in which the description of data as well as the measurement of the variables are presented. Section 3 describes the DCC and ADCC methodology. Section 4 presents the description of quantile regression. Section 5 briefly illustrated the threshold regression model. Section 6 demonstrated the empirical results. Lastly, section 7 concludes the study with policy implications.

Section snippets

Data and methods

Consistent with prior studies (Dutta, 2019; Mensi et al., 2022), we use two different indexes of solar energy indexes that track the equity prices of firms that produce electricity with solar energy, i.e. Ardour Solar Energy Index (SOLRX) and NASDAQ OMX Solar (GRNSOLAR). The SOLRX includes 27 companies from all around the world involved in the production of Photovoltaics, Solar Thermal, and Solar Lighting, with the majority of firms from the USA (38%) and China (21%). The GRNSOLAR is a

DCC and ADCC model

The multivariate GARCH models, i.e., DCC (Engle, 2002) and ADCC (Cappiello et al., 2006) are employed in the study to assess the volatility transmission and shocks between silicon stocks, lithium stocks, rare metal stocks, VIX, OVX, and solar energy stocks. DCC-GARCH model has three advantages over other volatility estimation models. First, the DCC-GARCH model can reveal the time-varying interdependence in the volatility of one variable on another. Second, the model includes other explanatory

Quantile regression

Quantile regression is extensively used in economics and finance literature to model the quantile of a random variable as a function of observed variables. Especially for economic and financial variables, this econometric technique receives significant attention due to estimating the coefficients in the presence of skewness, heteroskedasticity, and outliers. The baseline model to estimate the relationship between solar energy stocks, silicon stocks, lithium stocks, rare metal stocks, VIX, and

Threshold regression

We further investigate if the effect of lithium, silicon, and rare earth metals market on solar energy stocks varies depending on the level of market sentiments. Accordingly, we apply threshold regression by Hansen (2000) which allows the parameters to differ across regions (regimes) based on the value of threshold variables (VIX in our case). The multivariate threshold regression model by Hansen (2000) is better than Tong (2012) as it allows multiple predictors along with the lagged values of

DCC and ADCC estimations

Fig. 1, Fig. 2 exhibit the dynamic conditional correlation of renewable energy indices (SOLARX & GRNSOLAR with demand-side markets (SIL, LITH, and REM), oil price volatility (OVX), and investor sentiments Index (VIX). Similarly, Fig. 3, Fig. 4 shows the asymmetric dynamic conditional correlation of these variables. From these graphs, it is evident that the conditional correlations of solar energy indices and metals used in solar panels are highly positive, while the conditional correlations of

Conclusion, implications and recommendation

Diversifying solar energy stocks is important for investor to de-carbonize their portfolio. Insufficient incentives from clean energy stocks will shift the investor's attention back to the conventional markets which will adversely affect the low-carbon economy agenda. Thus, investigating clean energy stocks, their possible diversification potential, and their interconnectedness with other markets is of paramount importance to ethical investors. Unlike prior studies, our study analyzed the


Chien-Chiang Lee is grateful to the National Social Science Foundation Key Project of China for financial support through Grant No: 22AJL004.

Author contributions

Farzan Yahya: Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Software; Validation; Visualization; Roles/Writing - original draft., Ghulam Abbas: Formal analysis; Methodology; Project administration; Resources; Software; Validation; Visualization; Writing - review & editing, Chien-Chiang Lee: Formal analysis; Methodology; Project administration; Resources; Validation; Writing - review & editing.

© 2023 Published by Elsevier Ltd.


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