The Dynamics of Carbon on Green Energy Equity Investment: Utilizing the Quantile-on-Quantile and Quantile Coherency Approach

16 we analyze the dynamic correlation between the carbon price and the stock returns 17 of green energy companies and calculate the hedging effect of the carbon price on stock 18 returns in green energy sectors. The results show that the coefficients of the carbon 19 price change with time and are vulnerable to extreme events like the COVID-19. The 20 quantile-on-quantile (QQ) model results reveal a dynamic effect from the carbon price 21 to the stock returns of green energy sectors. The quantile coherency (QC) approach 22 results show that investors can benefit more in the short term with high-frequency 23 trading to hedge between carbon trading and the green energy stock market. What’s 24 more, the hedging effects are heterogenetic and investors should adjust their hedging 25 strategies in different quantiles.


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Green energy, defined by Walker and Devine-Wright (2008), includes the energy 33 produced sustainably from biomass and that produced by indefinitely regenerated 34 sources, like hydropower, solar, and wind energies. With the awareness that coal, oil, 35 and gas are the major causes of pollution and lead to environmental degradation, the 36 green energy sectors have become vertical to the global economy in the past decade 37 (Khurshid and Deng, 2020). The growth of energy demand and the constraints of 38 reduced carbon emissions will make it more challenging for the global economy to 39 achieve green growth (Wang et al., 2020). 40 Compared with traditional fossil energy, the resource scale of green energy is 800 41 times that of the former. Therefore, the attributes of manufacturing are far greater than 42 the attributes of resources, which will promote the manufacturing industry to better play 43 its advantages in photovoltaic, wind power, lithium battery, and hydrogen energy 44 industries. After generating economies of scale and technological iteration, energy costs 45 will be further reduced, bringing more economical costs. Some research finds that with 46 the carbon price rising, investments in green energy firms would be encouraged (Kumar 47 et al., 2012). While carbon emission rights trading covers multiple high-emission and 48 high-energy-consuming industries such as electricity, steel, heating, truth and oil 49 refining, etc. Therefore, the carbon price affects the upgrading and transformation of 50 these industries, which in turn affects the stock returns of listed companies in these 51 industries. The carbon price is the major factor to be considered when a pollution-   The relationship between the carbon price and stock markets has been widely 77 discussed since carbon trading becomes the most cost-effective emission reduction tool 78 to deal with climate change (see Moreno  reveal an asymmetric relationship between the carbon price and stock returns in China.

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A rise in carbon price shows a higher spillover effect on the stock market than a decrease

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The remainder of this paper is as follows. Section 3 introduces the main methodology 138 utilized in this paper. Section 4 shows the dataset and some preliminary results based 139 on the raw data. Section 5 illustrates the dynamics of the carbon price and stock returns 140 of the green energy market from a quantile perspective. Section 6 concludes the paper.  144 We first adopt the quantile regression model 1 to get some basic results in measuring 145 the dynamic effects of the carbon price on the stock returns of green energy sectors:

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The optimal hedge ratios (OHRs) that minimize the conditional variance trading market can be calculated as follows:  The WIND index is selected to delegate the clean energy wind sector and we use ISE Global Wind Energy Index as a proxy for this index, which tracks public companies that are active in the wind energy industry based on analysis of the products and services offered by those companies.
4   Table 2. Quantile results for the carbon price on the stock return of green energy sector.

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In this part, we use the quantile-on-quantile method to analyze the varying effect of 289 the carbon price returns on the green energy markets with results in Fig.4. It can be seen 290 that the effects change with quantiles and the effect shows heterogenetic and 291 asymmetrical characters following Hammoudeh (2014).

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As for the EUA-ERIX pair, the overall effects of the carbon price on the green energy  Besides, as the green energy is in high quantile, the effect shows an inverted U-shaped 305 distribution. At the middle quantile of the carbon, the slope coefficient is the highest.

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An asymmetric character can be seen from this pair.   Fig.6. Based on our sample data, we choose different rolling 355 windows. In Fig.6, (600, 30) means that the model is fitted by 30 observations with 600 356 one-period-ahead forecasts.

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Besides, we can obtain some revelations from Fig.6 (a). The ratio tells us how many