# Climate risks and the realized higher-order moments of financial markets: Evidence from China

Yihan Wang, Stephane Goutte, Elie Bouri, Amin Sokhanvar

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Yihan Wang, Stephane Goutte, Elie Bouri, Amin Sokhanvar. Climate risks and the realized higher-order moments of financial markets: Evidence from China. International Review of Economics and Finance, 2024, 93, pp.1064-1087. 10.1016/j.iref.2024.04.042. hal-04684212

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# Climate risks and the realized higher-order moments of financial markets: Evidence from China

Yihan Wang<sup>a</sup>, Stephane Goutte<sup>b, *</sup>, Elie Bouri<sup>c</sup>, Amin Sokhanvar<sup>d</sup>

## ARTICLE INFO

**Keywords:**

Chinese climate risks

Green energy

Carbon emission allowances

Oil and gas energy

Time-varying granger causality

Realized higher-order moments

## ABSTRACT

The return distribution of energy assets deviates from the normal distribution, making volatility a partial measure of risk, and climate risk tend to affect the skewness preferences of environmentally conscious investors. However, limited evidence exists on the impact of climate risk on higher-order moments of asset returns, especially in China. Using data from December 2013 to June 2022, we study the time-varying Granger causal flow from two Chinese climate uncertainty indices (Chinese climate policy uncertainty (CPU) and Chinese climate uncertainty (CU)) to the realized higher-order moments (volatility, skewness, and kurtosis) of various Chinese assets covering green energy, carbon emission allowances, and brown energy indices. The main results show evidence of time-variation in the causal flows from climate risks, without ignoring the heterogeneity between the two climate risk measures and across the three assets under study. Granger causality spans various time periods, and is generally more significant for the realized volatility. These results are robust to the specification of the time-varying causality analysis. They also remain significant when accounting for the impact of Chinese economic policy uncertainty, which further supports their robustness. These findings highlight the ability of climate risk to affect higher-order moments of asset returns through the channels of strong deviation of returns from the normal distribution, the preference of investors for skewness, and the frequent occurrence of crisis periods.

## 1. Introduction

The impact of climate risks on asset returns and volatility, portfolio allocation, risk management, and market regulation has become a major topic in recent research (see, among others, Bouri et al., 2022, 2023; Dutta et al., 2023; Guo et al., 2022; Xu et al., 2022; Li et al., 2023). In this regard, it is often argued that the decisions of environmentally conscious investors and their pro-environmental preferences for green energy assets to the detriment of brown energy assets are shaped by the high uncertainty

<footnote><sup>a</sup> School of Accounting, Nanjing University of Finance and Economics, Wenyuan Road No.3, Qixia District, Nanjing, Jiangsu Province, 210023, China</footnote>

<footnote><sup>b</sup> UMI SOURCE, University Paris Saclay, UVSQ, IRD, France and Paris School of Business, 59 rue nationale, 75013, Paris, France</footnote>

<footnote><sup>c</sup> School of Business, Lebanese American University, Lebanon</footnote>

<footnote><sup>d</sup> Cambridge Resources International, Kingston, Ontario, Canada</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: evawangyihan@163.com (Y. Wang), stephane.goutte@uvsq.fr (S. Goutte), elie.elbouri@lau.edu.lb (E. Bouri), sokhanvar@cri-world.com (A. Sokhanvar).</footnote>
surrounding the climate, high global temperatures, harsh weather, and accelerating environmental degradation (Choi et al., 2020; Pástor et al., 2020).<fnref n="1" /> Furthermore, Bouri et al. (2022) highlight the significant impact of climate policy uncertainty, constructed by Gavirilidis (2021) using textual analysis of several US newspapers, on the dynamics of green and brown energy stock prices in the US. Similarly, Dutta et al. (2022) show that climate policy uncertainty is positively (negatively) associated with the return (volatility) green energy assets, and that rising climate policy uncertainty is associated with a decrease in the conditional correlation between crude oil and green energy returns. They further indicate that green assets hedge more effectively crude oil returns than gold. Zeng et al. (2022) indicate that climate policy uncertainty has a higher forecasting power for the volatility of the carbon-neutral index in China than economic policy uncertainty.

The related literature on this topic, although growing, is restricted on three fronts. Firstly, most previous studies dealing with the impact of climate risk on financial markets are conducted in the US and Europe (Bouri et al., 2022, 2023; Dutta et al., 2023; Li et al., 2023). Little attention has been given to Chinese financial markets, although China is the second largest economy and has the second largest stock markets after the US. However, Chinese environmental policymaking and environmentally conscious investors are concerned with the impact of Chinese climate risk on local financial markets. Secondly, the few studies focusing on China (e.g., Dai & Zhang, 2023; Ren, Qin, & Dong, 2022; Ren, Zhang, et al., 2022; Zeng et al., 2022) use climate risk measures constructed based on global data covering developed economies in the US and Europe, such as the climate policy uncertainty of Gavirilidis (2021), without considering climate risk measures exclusively reflecting the Chinese context (e.g., Lee & Cho, 2022). This is surprising given that China is the largest emitter of carbon dioxide; and notably the uncertainty surrounding its climate policies can be different and possibly higher than that of developed economies, which suggest the possibility of Chinese climate risks having a different impact on Chinese financial markets. Thirdly, the existing literature considers the impact of climate risk on the return and volatility of financial markets, ignoring higher-order moments such as skewness and kurtosis. However, we argue that due to the strong deviation of asset returns from the Gaussian distribution, asset volatility is a partial measure of risk, which ultimately requires the inclusion of skewness and kurtosis in the analysis (Bouri, 2023; Zhang et al., 2023) to capture risk in a more comprehensive way.

In this paper, we build on the above lines of research by examining the impact of Chinese climate risk on the realized volatility, realized skewness, and realized kurtosis of Chinese financial markets. We consider two measures of Chinese climate risk, Chinese climate policy uncertainty (CPU) and Chinese climate uncertainty (CU) and various indices from the Chinese financial markets, covering green energy, carbon emissions, and brown energy. This potentially allows us to capture two types of heterogeneity, the heterogeneity of the impact between CPU and CU, and the heterogeneous influence of climate risk on green and non-green energy assets.

Therefore, we answer the following research questions.

**H1.** Do Chinese climate risks translate into green and non-green Chinese asset realized higher-moments?

**H2.** If so, do they exhibit a time-variation in the impact reflecting the occurrence of various crisis periods such as the COVID-19 outbreak and Russia-Ukraine war?

**H3.** Do Chinese climate risks affect the realized higher-order moments of green and non-green Chinese assets in a similar way?

The study of Chinese climate risks is made possible by the construction of two Chinese climate risk indices by Lee and Cho (2022). Our current analysis is somewhat related to the literature studying the impact of climate risk on global energy assets (Shang et al., 2022; Ren, Li, He, & Lucey, 2023,<fnref n="2" />; Xu et al., 2022).

The channels through which climate risk can affect volatility, skewness, and kurtosis are described as follows. The first channel through which climate risk can affect asset volatility, skewness, and kurtosis is the strong deviation of asset returns from the normal distribution. In fact, volatility is not a complete measure of risk under non-normally distributed returns, thus risk can manifest not only in volatility but also in higher-order moments such as skewness and kurtosis. In this regard, the related literature is suboptimal because it examines the impact of climate risk on asset volatility only, which leaves room for considering the impact of climate risk on skewness and kurtosis, somewhat reflecting return asymmetry and fat tails (Bouri, 2023; Zhang et al., 2023). Secondly, the preference of investors for skewness is very well known (Lambert & Hübner, 2013). This should not be an exception for environmentally conscious investors,<fnref n="3" /> which makes global risk factors, such as climate risk, relevant to the choices of investors and thus to the higher-order moments of various assets such as green and brown energy assets and carbon emission allowances. Thirdly, the occurrence of crisis periods can shape the distribution of returns and deviate it from the normal distribution through its effect on skewness and kurtosis. Coupled with this is the emergence of climate risk as a global risk factor, raising a major concern for the decisions of investors and thus to asset returns. The coexistence of these factors implies the possibility of an impact of climate risk on the higher-order moments of financial markets. Notably, following COVID-19 and especially after the start of the Russia-Ukraine war,<fnref n="4" /> many governments have eased their reliance on natural gas and turned back to more polluting fossil fuels such as coal, leading to an environmental degradation,

<footnote n="1">¹ Investors are concerned with carbon risk (Bolton & Kacperczyk, 2019), whereas firm decisions are affected by climate risk (Engle et al., 2020; Lopez et al., 2017).</footnote>

<footnote n="2">² It is worth noting that Ren, Li, He, and Lucey (2023) apply a time-varying Granger causality test but limit their analysis to return volatility.</footnote>

<footnote n="3">³ Although ecologically conscious investors care about the environment when making their investment decisions, they face the risk-return tradeoff and seek profit opportunities. Therefore, they have a tendency towards positively skewed outcome distributions.</footnote>

<footnote n="4">⁴ There is evidence of financial contagion during the pandemic (Akhtaruzzaman et al., 2021), and the evolution of finance research following the COVID-19 outbreak is documented (Boubaker et al., 2023). The impact of war in Ukraine on global stock markets is noted (Boubaker et al., 2022a) and other related studies consider the impact of border disputes on stock sectors (Hassan et al., 2022b).</footnote>
more climate risk and concern, and ultimately a higher demand for carbon emission allowances.<fnref n="5" /> This should in turn affect the price and volatility of carbon emission allowances, and possibly their higher-order moments such as skewness and kurtosis, which further justify the relevance of considering the potential impact of climate risk on higher-order moments of assets returns.

Concerning the potential dissimilarity in the impact of climate risks on green and non-green assets, we argue, firstly, that the preferences of investors engaging in these assets are different, including not only their return-risk preferences but also their skewness preferences. Furthermore, the existing literature provides evidence that environmentally conscious investors might accept less attractive risk-adjusted returns and ultimately negatively skewed green energy asset returns if these assets are beneficial to the environment and society and contribute to portfolio decarbonization under the transition to cleaner energy. Secondly, the potential dissimilarity in the impact of the two climate risks can arise from their construction; as argued by Lee and Cho (2022) and described in the data section, CU reflects the physical aspect of Chinese climate risk covering floods and heatwaves, whereas CPU is more comprehensive through its reflection of both physical and transitional climate risk in China.

Our first contribution in this paper is to examine the causal flow from two Chinese climate risk measures to the realized high-order moments of Chinese green and non-green energy assets. To the best of our knowledge, there is no single study examining the potential impact of climate risk on the higher moments of (Chinese) financial assets. The second contribution consists of examining the time-varying effect of climate risks, accounting for crisis periods and potential shift in the causal flow across time. These issues are very relevant to the second largest economy, China, where the carbon emission market is still young yet promising and notably the awareness of the impact of climate risk should be improved to cover the circle of investors, especially individual investors who are predominant in China. The third contribution reflects our differentiation between green and non-green Chinese assets when it comes to their reaction to climate risks. In this regard, environmentally responsible investors are very sensitive to carbon risk (Bolton & Kacperczyk, 2019) and empirical evidence suggests that green energy assets outperform non-green energy assets when the uncertainty about climate policies is high (see, Pástor et al., 2020; Choi et al., 2020; Bouri et al., 2022).<fnref n="6" />

Our analysis is related to the strand of literature considering higher-order moments of asset returns for portfolio allocation and risk management (see, Martellini & Ziemann, 2010; Kim et al., 2014). It is also related to previous studies on the spillovers of skewness and kurtosis and the factors that can affect them (Zhang et al., 2023). Our study is also related to Hsu et al. (2020) who provide a link between uncertainty in climate policies and emission portfolio returns and to the literature on the performance of brown (i.e. polluting) companies (Bolton & Kacperczyk, 2019).

The main results are summarized as follows. Firstly, we highlight the significance of the causal flow from both CU and CPU to the realized skewness of the indices under study, with impacts that span various time periods. Secondly, the causal flows from the two climate risk measures to the realized volatility of the indices under study are more pronounced than the causal flow to the realized skewness and kurtosis. These results are robust to the use of both rolling window and recursive evolving Granger causality tests, which emphasizes the robustness of our findings to the specification of the time-varying causality analysis. Thirdly, we account for the impact of Chinese economic policy uncertainty and show that most of the significant causal flow from CPU and CU to the higher-order moments of the three Chinese assets remains statistically significant over several time periods, which supports the credibility, robustness, and validity of the causal relationships identified in our empirical analysis.

Given that policymakers and financial regulators are spending efforts to refine their understanding of how climate risks influence firms, market participants, and financial markets, our analysis should be useful to policymakers and regulators seeking more granular and precise information for supervisory resolutions to mitigate climate risk.

The rest of the paper is structured as follows: Section 2 describes the time-varying Granger causality test. Section 3 presents the dataset and the construction of the realized higher-order moments. Section 4 presents the main results and an extended analysis accounting for the impact of Chinese economic policy uncertainty. Section 5 concludes the study.

## 2. Time-varying granger causality tests

To accommodate the time-varying Granger causality test between climate risk and the three realized higher moments of the indices under study, we employ the approach of Shi et al. (2018, 2020) in which rolling and recursive evolving (time-varying) Granger causality tests are conducted.<fnref n="7" /> Following Shi et al. (2018, 2020), an unrestricted VAR(k) in multivariate regression format is:

$$y_t = \Pi x_t + \varepsilon_t, t = 1, \dots, T \qquad (1)$$

where $y_t = (y_{1t}, y_{2t})'$, $x_t = (1, y_{t-1}', y_{t-2}', \dots, y_{t-k}')'$, and $\Pi_{2 \times (2k+1)} = [\psi_0, \psi_1, \dots, \psi_p]$.

<footnote n="5">⁵ https://climate.ec.europa.eu/news-your-voice/news/eu-carbon-market-report-driving-emission-reductions-and-enabling-climate-and-energy-investment-2022-12-14_en.</footnote>

<footnote n="6">⁶ On a related front, Belaïd and Creti (2021) consider the transition to cleaner energy production, climate change, and the pandemic. Belaïd (2022) highlights the implications of poorly designed climate policy on energy poverty. Belaïd et al. (2023) focus on the role of green investments in balancing climate mitigation and energy security objectives.</footnote>

<footnote n="7">⁷ Shi et al. (2020) argue that time-varying Granger causality tests belong to the literature dealing with the instability in the model parameters, which cover the Markov-switching Granger causality test of Psaradakis, Ravn, and Sola (2005) and the tests of Rossi (2005). However, the test of Shi et al. (2018) can be applied in a real-time manner, which makes it different from the other tests often applied following ex-post testing procedures. The latter tests tend to experience difficulty in detecting breaks located near the end of the sample.</footnote>
<table><caption>**Fig. 1.** Time-varying causality from CPU and CU to realized volatility (RV)</caption><thead><tr><th>Granger causal flow from CPU to RV</th><th>Granger causal flow from CU to RV</th></tr></thead><tbody><tr><td colspan="2">Panel A: Rolling window</td></tr><tr><td><p>Rolling Wald test for CARBON_RV G-caused by CPU, 2014m1 - 2022m6<br>with 90th (-) and 95th (-) percentiles of bootstrapped test statistics</p></td><td><p>Rolling Wald test for CARBON_RV G-caused by CU, 2014m1 - 2022m6<br>with 90th (-) and 95th (-) percentiles of bootstrapped test statistics</p></td></tr><tr><td><p>Rolling Wald test for GREEN_RV G-caused by CPU, 2014m1 - 2022m6<br>with 90th (-) and 95th (-) percentiles of bootstrapped test statistics</p></td><td><p>Rolling Wald test for GREEN_RV G-caused by CU, 2014m1 - 2022m6<br>with 90th (-) and 95th (-) percentiles of bootstrapped test statistics</p></td></tr><tr><td><p>Rolling Wald test for OILANDGAS_RV G-caused by CPU, 2014m1 - 2022m6<br>with 90th (-) and 95th (-) percentiles of bootstrapped test statistics</p></td><td><p>Rolling Wald test for OILANDGAS_RV G-caused by CU, 2014m1 - 2022m6<br>with 90th (-) and 95th (-) percentiles of bootstrapped test statistics</p></td></tr></tbody></table>

Notes: Chinese climate policy uncertainty (CPU). Chinese climate uncertainty (CU). Realized volatility (RV). Upper and lower dashed lines represent 5% and 10% bootstrapped critical values. The size of the window is 30 months.
Panel B: Recursive expanding

<figure><img src="image_6.png" /><figcaption>Fig. 1. (continued).</figcaption></figure>
Let $\hat{\Pi}$ be the ordinary least squares estimator of $\Pi$, $\hat{\Omega} = T^{-1}\hat{\epsilon}_t\hat{\epsilon}_t'$ with $\hat{\epsilon}_t = y_t - \hat{\Pi}x_t$, and $X' = [x_1, \dots, x_T]$. The Wald test of the constraints required by the null hypothesis used to test that $y_{2t}$ does not Granger cause $y_{1t}$ is given by:

$$W = [\mathrm{Rvec}(\hat{\Pi})]' [R(\hat{\Omega} \otimes (X'X)^{-1})R']^{-1} [\mathrm{Rvec}(\hat{\Pi})] \quad (2)$$

where $\mathrm{vec}(\hat{\Pi})$ denotes the row vectorization, $\otimes$ is the Kronecker product, and $R$ is a $m \times n^2k$ matrix. The Wald statistic has the asymptotic null distribution $x_m^2$ under the null hypothesis where $m$ is the number of restrictions.

Shi et al. (2018, 2020) propose a time-variation in the Granger causality test based on the supremum (sup) Wald statistic sequences using a forward recursive (Thoma, 1994), rolling window (Swanson, 1998), and recursive evolving algorithm (Phillips et al., 2015). A significant change in Granger causality is identified if the Wald statistic sequence exceeds its corresponding bootstrapped 5% critical value.

For the recursive evolving procedure, we denote by $W_{f_2}(f_1)$ the Wald statistic over $[f_1, f_2]$ with a sample size fraction of $f_w = f_2 - f_1 \ge f_0$. The sup Wald statistic is defined as:

$$\mathcal{S}\mathcal{W}'_f(f_0) = \sup_{(f_1, f_2) \in \Lambda_0} \{\mathcal{W}'_f(f_1)\} \quad (3)$$

where $\Lambda_0 = \{(f_1, f_2): 0 < f_0 + f_1 \le f_2 \le 1$, and $0 \le f_1 < 1 - f_0$} for some minimal sample size $f_0 \in (0, 1)$ in the regressions.

The dating rules of the rolling and recursive evolving algorithms are presented as follows:

$$\text{Rolling: } \hat{f}_e = \inf_{f \in [f_0, 1]} \{f: \mathcal{W}'_f(0) > \text{cv}\} \quad \text{and} \quad (4)$$

$$\hat{f}_f = \inf_{f \in [f_e, 1]} \{f: \mathcal{W}'_f(0) < \text{cv}\}$$

$$\text{Recursive Evolving: } \hat{f}_e = \inf_{f \in [f_0, 1]} \{f: \mathcal{S}\mathcal{W}'_f(0) > \text{scv}\} \quad \text{and} \quad (5)$$

$$\hat{f}_f = \inf_{f \in [f_e, 1]} \{f: \mathcal{S}\mathcal{W}'_f(0) < \text{scv}\}$$

where $\hat{f}_e$ and $\hat{f}_f$ are the estimated first chronological observations whose test statistics respectively exceed or fall below the critical values for the origination point ($f_e$) and termination point ($f_f$) in the Granger causal relationship, and $cv$ and $scv$ are the corresponding critical values of the $\mathcal{W}'_f$ and $\mathcal{S}\mathcal{W}'_f$ statistics. For multiple switches, the origination and termination dates are calculated in a similar fashion.

Given empirical evidence from Shi et al. (2020) that the recursive evolving window and rolling window approaches offer the highest power, we employ these two approaches while applying the time-varying Granger causality test.

The lag orders for the estimation of the bivariate VAR model and Granger causality test are selected using the Bayesian information criterion (BIC) with a maximum length of 12. The minimum window size $f_0$ is 30 months. The critical values are taken from a bootstrapping procedure with 499 replications.

### 3. The dataset

This study uses two datasets. The first is the Chinese climate policy uncertainty (CPU) and Chinese climate uncertainty (CU) indices, which are available at monthly frequency and collected from the website of professors Kiryoung Lee and Juik Cho (https://sites.google.com/view/twitter-chn-epu/home). These indices capture various aspects of climate uncertainty in China. CU echoes tweets about climate uncertainty in China based on three types: (1) “China”, (2) “climate change”, and (3) “uncertainty”, and thereby reflects somewhat the physical aspects of climate risk covering floods and heatwaves. CPU reflects tweets on these three types and a fourth, “climate policy uncertainty”, thus capturing both physical and transitional (policy and regulation) climate risk. As argued by Lee and Cho (2022), both climate uncertainty indices “spike with climate change-related president announcements, UN climate change conferences, climate change-related flood deaths, warnings about melting glaciers in China’s Qilian mountains, and rising climate concerns regarding Bitcoin mining, among many others”. Accordingly, these two measures of Chinese climate risk can influence financial markets and the investment decisions and risk inferences of market participants. In our analysis, the levels of CPU and CU plotted in Appendix Figure A1 are used. This is because the level of the climate risk should matter more to investors, and thus to their investment decisions and the switch between various energy assets, than the change in the levels.<fnref n="8" />

<footnote n="8">⁸ For example, suppose the CPU at $t_1$ is 100 and $t_2$ is 200, then at $t_2$ CPU return is 69.31%. Alternatively, suppose at $t_{100}$, CPU is 1500, and at $t_{101}$ CPU is 1700. So at $t_{101}$, CPU return is 12.51%. Intuitively, the CPU should have a larger impact on asset prices at $t_{101}$ although CPU return is much lower.</footnote>
<figure><img src="image_7.png" /><figcaption>**Fig. 2.** Time-varying causality from CPU and CU to realized skewness (RS)</figcaption></figure>

Notes: Chinese climate policy uncertainty (CPU). Chinese climate uncertainty (CU). Realized skewness (RS). Upper and lower dashed lines represent 5% and 10% bootstrapped critical values. The size of the window is 30 months.
Panel B: Recursive expanding

<figure><img src="image_8.png" /><figcaption>Fig. 2. (continued).</figcaption></figure>
The second dataset includes three Chinese indices, the green energy stock index (CEN), carbon emission allowance prices (CEA), and the gas and oil stock index (BEN), collected from the Wind database at the daily frequency. Notably, the gas and oil stock index represents the brown energy market (see, Bouri et al., 2022). The sample period is December 2013 to June 2022, as dictated by the availability of the dataset. The sample period is rich and long enough to reflect crisis periods and extreme events, such as the 2015–2016 Chinese stock market turbulence, COVID-19 pandemic, and Russia-Ukraine war. Interestingly, CO2 emissions have increased since the COVID-19 pandemic and particularly after the outbreak of the Russia-Ukraine war. Since the war and the imposed sanctions, many countries in Europe have reduced their dependence on Russian natural gas and crude oil, leading to an increase in the production and consumption of dirty energy. Figure A2 plots the realized higher-order moments, whereas Figure A3 plots the log returns.

After computing the daily log returns of the three Chinese indices, we construct their monthly realized high-order moments (realized volatility, realized skewness, and realized kurtosis) based on daily log returns $r_{i,t}$ ($r_{i,t} = \ln(p_{t,i}/p_{t-1,i})$; $i = 1, 2, ..., N$; where $N$ is the total number of daily log returns during the trading month, $t$) as follows. Realized volatility ($RV_t$) is the sum of the square of daily returns, constructed based along the lines of Barndoff-Nielsen et al. (2010), as:

$$RV_t = \sum_{i=1}^{N} r_{i,t}^2. \qquad (1b)$$

Realized skewness $RS_t$ and realized kurtosis $RK_t$ are constructed following Amaya et al. (2015):

$$RS_t = \frac{\sqrt{N} \sum_{i=1}^{N} r_{t,i}^3}{RV_t^{3/2}}, \qquad (2b)$$

$$RK_t = \frac{N \sum_{i=1}^{N} r_{t,i}^4}{RV_t^2}. \qquad (3b)$$

Table 1 presents the summary statistics of the CPU and CU levels, and the realized moments of carbon allowances, green energy and oil and gas indices, which are all at the monthly frequency. Considering the two Chinese climate risk measures, we note that the standard deviation is the highest for CPU. Regarding the three assets under study, CEA has the highest volatility. Except the climate risk measures, the higher-order moments of most of the assets deviate from normal distributions, as indicated by the Jarque-Bera statistics.

# 4. Results of the time-varying granger causality test

## 4.1. Causal flow from climate risk to realized moments

Given that the approach of Shi et al. (2018, 2020) does not necessitate pre-filtering the data, only the maximum order of integration of the VAR, we examine the stationarity of the variables by applying various unit root tests. The results show that all realized higher-order movements under study are integrated of order 1, and the same is true for the two climate indices (see Table 1). We apply the time-varying Granger causality approach based on a lag augmented VAR model with $d = 1$. Specifically, we consider both rolling window and recursive expanding approaches, given that these two approaches provide the most reliable results (see Shi et al., 2018).

Figs. 1–3 plot the results of the time-varying causal flow from each climate risk to RV, RS, and RK, respectively. The Granger causality is significant if the Wald statistics exceed the bootstrapped 10% and 5% critical values. We report the results of the causal flow based on the rolling window in Panel A and based on recursive expanding in Panel B.

Starting with Fig. 1, there are time-varying Wald test statistics for the Granger causality from CPU (left panel) and CU (right panel) indices to the RV of CEN, CEA, and BEN. This is shown along with their bootstrapped critical values obtained from the rolling window and recursive expanding techniques.<fnref n="9" /> The test statistics of the causal flow from CPU to CEA are above their bootstrapped critical values, implying that the null hypothesis of no Granger causality is rejected at conventional statistical levels.

Fig. 1 Panel A illustrates the outcomes of a rolling window Granger causality test for RV. The causal flow from CPU to the realized volatility of carbon emission allowance prices is statistically significant at the 5% level for nearly the entire duration of the observed sample period. The causal flow from CU is statistically significant from the year 2018 onwards, except for periods around the zenith of the COVID-19 pandemic and throughout most of 2020. Furthermore, the causality from CPU to the realized volatility of the Chinese green energy stock index displays significant influence at the 5% level during 2016 and the initial months of 2017. Moreover, this causal relationship remains significant for a substantial portion of the period from 2018 to the conclusion of 2020. In contrast, the causal flow from CU to the realized volatility of the Chinese green energy stock index demonstrates a statistically significant impact at the 10% level throughout 2018. Likewise, the causal effect of CPU on the realized volatility of the Chinese gas and oil stock index is significant at the 10% level during the year 2016 and around the peak of the COVID-19 pandemic. Notably, the causal flow from CU to

<footnote n="9">⁹ The estimation is done with the homoscedastic-consistent subsample Wald test statistic, however, the robustness of the above-mentioned results is assessed using the heteroscedastic-consistent subsample Wald test statistic.</footnote>
<table><caption>**Table 1**
Summary statistics of monthly climate risk and monthly realized moments.</caption><thead><tr><th></th><th>Mean</th><th>Maximum</th><th>Minimum</th><th>Standard Deviation</th><th>Skewness</th><th>Kurtosis</th><th>Jarque-Bera</th><th>ADF</th><th>PP</th></tr></thead><tbody><tr><td colspan="10"><strong>Climate risk</strong></td></tr><tr><td>CPU</td><td>130.452</td><td>2176.396</td><td>0.000</td><td>274.221</td><td>5.263</td><td>35.781</td><td>130.452</td><td>-9.831***</td><td>-9.980***</td></tr><tr><td>CU</td><td>136.935</td><td>769.281</td><td>4.048</td><td>158.239</td><td>2.011</td><td>6.925</td><td>136.935</td><td>-6.900***</td><td>-7.155***</td></tr><tr><td colspan="10"><strong>Realized volatility</strong></td></tr><tr><td>CEA</td><td>2.291</td><td>30.703</td><td>0.012</td><td>5.437</td><td>3.366</td><td>14.206</td><td>726.336***</td><td>-4.676***</td><td>-4.674***</td></tr><tr><td>CEN</td><td>0.008</td><td>0.041</td><td>0.001</td><td>0.008</td><td>2.294</td><td>8.929</td><td>238.845***</td><td>-4.969***</td><td>-4.933***</td></tr><tr><td>BEN</td><td>0.005</td><td>0.041</td><td>0.000</td><td>0.007</td><td>2.797</td><td>12.448</td><td>512.389***</td><td>-5.081***</td><td>-4.99***</td></tr><tr><td colspan="10"><strong>Realized skewness</strong></td></tr><tr><td>CEA</td><td>0.066</td><td>3.959</td><td>-2.427</td><td>0.983</td><td>1.219</td><td>6.900</td><td>89.902***</td><td>-9.302***</td><td>-9.196***</td></tr><tr><td>CEN</td><td>0.034</td><td>1.659</td><td>-3.056</td><td>0.854</td><td>-0.465</td><td>3.438</td><td>4.496*</td><td>-10.135***</td><td>-10.171***</td></tr><tr><td>BEN</td><td>-0.157</td><td>2.012</td><td>-3.221</td><td>0.996</td><td>-0.041</td><td>2.639</td><td>0.581</td><td>-10.294***</td><td>-10.339***</td></tr><tr><td colspan="10"><strong>Realized kurtosis</strong></td></tr><tr><td>CEA</td><td>4.361</td><td>18.064</td><td>1.051</td><td>2.690</td><td>2.373</td><td>11.238</td><td>384.109***</td><td>-9.073***</td><td>-9.179***</td></tr><tr><td>CEN</td><td>3.191</td><td>11.746</td><td>1.597</td><td>1.259</td><td>3.439</td><td>22.553</td><td>1825.795***</td><td>-9.896***</td><td>-9.921***</td></tr><tr><td>BEN</td><td>3.519</td><td>13.323</td><td>1.562</td><td>1.490</td><td>3.109</td><td>19.724</td><td>1353.112***</td><td>-10.940***</td><td>-10.903***</td></tr></tbody></table>

Notes: The sample period is December 2013 to June 2022, yielding 102 observations. This table considers the monthly indices of CPU (Chinese climate policy uncertainty) and CU (Chinese climate uncertainty), and the monthly realized higher-moments of CEA (Chinese carbon allowances), CEN (Chinese green energy), BEN (Chinese brown energy, i.e. gas and oil index). ADF (augmented Dicky-Fuller), PP (Phillips-Perron) stationarity tests. * and *** indicate statistical significance at the 10% and 1% levels, respectively.

the realized volatility of the Chinese gas and oil stock index exhibits statistical significance at the 10% level throughout the second half of 2016, the entirety of 2017, the first half of 2019, the entirety of 2021, and initial months of 2022.

Fig. 1 Panel B shows the outcomes of a recursive evolving Granger causality test applied to RV. The causal flow from CPU to the realized volatility of carbon emission allowance prices attains statistical significance at the 5% level for nearly the entire duration of the analysed sample period. Additionally, the causal flow from CU becomes significant from the latter months of 2017 onwards, except for periods coinciding with the zenith of the COVID-19 pandemic and throughout 2020. The causal flow from CPU to the RV of the Chinese green energy stock index maintains significant statistical relevance at the 5% level across the entirety of the sample period. Conversely, the causal flow from CU to the RV of the Chinese green energy stock index demonstrates a statistically significant impact at the 10% level for the majority of 2018, as well as the initial months of 2021. Similarly, the causal impact of CPU on the RV of the Chinese gas and oil stock index remains statistically significant at the 10% level throughout the entire observed sample period, with exceptions around the beginning of 2018 and the commencement and middle of 2021. The causality from CU to the RV of the Chinese gas and oil stock index is statistically significant at the 10% level from the second half of 2016 until the conclusion of 2020, except for a period around the middle of 2018. This causal flow is also significant in 2021 and 2022.

Fig. 2 Panel A presents the results of the rolling window Granger causality involving RS. Notably, the causal linkage from CPU to the RS of carbon emission allowance prices achieves significance at the 10% level during the second half of 2018 and the initial half of 2022. Conversely, the causal impact attributed to CU is statistically significant at the 10% level from the second half of 2017 until the conclusion of 2021. Moreover, the causality from CPU to the RS of the Chinese green energy stock index attains significance at the 10% level around the middle of 2016 and during the initial months of 2021. Similarly, the causal relationship originating from CU demonstrates a statistically significant impact at the 10% level during the middle of 2019 and the initial months of 2022 with respect to the realized skewness of the Chinese green energy stock index. Equally importantly, the causal flow from CPU on the RS of the Chinese gas and oil stock index attains statistical significance at the 10% level during the first months of 2017, the middle of 2018, the end of 2019, and around the middle of 2020. Notably, there is no discernible causality from CU to the RS of the Chinese gas and oil stock index.

Fig. 2 Panel B shows the outcomes of a recursive evolving Granger causality analysis involving RS. Remarkably, the causal impact from CPU on the RS of carbon emission allowance prices is significant at the 10% level during the second half of 2018 and the initial half of 2022. Additionally, the causal effect attributed to CU achieves statistical significance at the 5% level, from the middle of 2017 until the conclusion of 2021. Furthermore, the causality from CPU to the RS of the Chinese green energy stock index maintains statistical significance at the 10% level in the middle of 2016 and the initial months of 2021. Similarly, the causal linkage attributed to CU demonstrates a statistically significant impact at the 10% level in the middle of 2019 and the initial months of 2022 in relation to the RS of the Chinese green energy stock index. Similarly, the causal impact of CPU on the RS of the Chinese gas and oil stock index is statistically significant at the 10% level during the first months of 2017, the middle of 2018, the last months of 2019, and middle of 2020. No causality is detected from CU to the RS of the Chinese gas and oil stock index.

Taken together, it appears that the causal flows from each of CU and CPU to the realized volatility of the series are notably more pronounced than their causal impacts on realized skewness.

Moving to Fig. 3 Panel A, we present the causality results based on a rolling window Granger causality analysis involving RK. Particularly, the causal flow from CPU and CU to the RK of carbon emission allowance prices is significant at the 10% level throughout almost the entire sample period. The causality from CPU to the RK of the Chinese green energy stock index achieves statistical significance at the 10% level during the second half of 2016, the initial half of 2017, the latter half of 2020, and the initial months of 2021. In parallel, the causal flow from CU demonstrates a statistically significant impact at the 10% level around the middle of 2019 and the
Granger causal flow from CPU to RK

Granger causal flow from CU to RK

<table><caption>Panel A: Rolling window</caption><tr><td><img src="image_0_0.png" alt="Rolling Wald test for CARBON_RK G-caused by CPU, 2014m1 - 2022m6 with 90th (-) and 95th (-) percentiles of bootstrapped test statistics"></td><td><img src="image_0_1.png" alt="Rolling Wald test for CARBON_RK G-caused by CU, 2014m1 - 2022m6 with 90th (-) and 95th (-) percentiles of bootstrapped test statistics"></td></tr><tr><td><img src="image_0_2.png" alt="Rolling Wald test for GREEN_RK G-caused by CPU, 2014m1 - 2022m6 with 90th (-) and 95th (-) percentiles of bootstrapped test statistics"></td><td><img src="image_0_3.png" alt="Rolling Wald test for GREEN_RK G-caused by CU, 2014m1 - 2022m6 with 90th (-) and 95th (-) percentiles of bootstrapped test statistics"></td></tr><tr><td><img src="image_0_4.png" alt="Rolling Wald test for OILANDGAS_RK G-caused by CPU, 2014m1 - 2022m6 with 90th (-) and 95th (-) percentiles of bootstrapped test statistics"></td><td><img src="image_0_5.png" alt="Rolling Wald test for OILANDGAS_RK G-caused by CU, 2014m1 - 2022m6 with 90th (-) and 95th (-) percentiles of bootstrapped test statistics"></td></tr></table>

<caption>Fig. 3. Time-varying causality from CPU and CU to realized kurtosis (RK)</caption>

Notes: Chinese climate policy uncertainty (CPU). Chinese climate uncertainty (CU). Realized kurtosis (RK). Upper and lower dashed lines represent 5% and 10% bootstrapped critical values. The size of the window is 30 months.
Panel B: Recursive expanding

<figure><img src="image_9.png" /><figcaption>Fig. 3. (continued).</figcaption></figure>
<figure><img src="image_10.png" /><figcaption>**Fig. 4.** Time-varying causality from CPU and CU to realized volatility (RV) accounting for EPU

Notes: Chinese climate policy uncertainty (CPU). Chinese climate uncertainty (CU). Realized volatility (RV). Upper and lower dashed lines represent 5% and 10% bootstrapped critical values. The size of the window is 30 months.</figcaption></figure>
Panel B: Recursive expanding

<figure><img src="image_11.png" /><figcaption>Fig. 4. (continued).</figcaption></figure>

initial months of 2022 in relation to the RK of the Chinese green energy stock index. Equally noteworthy is the causality from CPU to the RK of the Chinese gas and oil stock index, which attains statistical significance at the 10% level during the last months of 2016, the middle of 2017, and the years 2018 and 2019. Additionally, a discernible causality from CU to the RK of the Chinese gas and oil stock index is evident at the 5% level throughout the years 2016, 2017, and 2018.
<figure><img src="image_12.png" /><figcaption>**Fig. 5.** Time-varying causality from CPU and CU to realized skewness (RS) accounting for EPU

Notes: Chinese climate policy uncertainty (CPU). Chinese climate uncertainty (CU). Realized skewness (RS). Upper and lower dashed lines represent 5% and 10% bootstrapped critical values. The size of the window is 30 months.</figcaption></figure>
Panel B: Recursive expanding

<table><caption>Fig. 5. (continued).</caption><tr><td>Recursive expanding Wald test for CARBON_RS G-caused by CPU, 2014m1 - 20 with 90th (-) and 95th (-) percentiles of bootstrapped test statistics</td><td>Recursive expanding Wald test for CARBON_RS G-caused by CU, 2014m1 - 20 with 90th (-) and 95th (-) percentiles of bootstrapped test statistics</td></tr><tr><td>Recursive expanding Wald test for GREEN_RS G-caused by CPU, 2014m1 - 202 with 90th (-) and 95th (-) percentiles of bootstrapped test statistics</td><td>Recursive expanding Wald test for GREEN_RS G-caused by CU, 2014m1 - 202 with 90th (-) and 95th (-) percentiles of bootstrapped test statistics</td></tr><tr><td>Recursive expanding Wald test for OILANDGAS_RS G-caused by CPU, 2014m1 - 2 with 90th (-) and 95th (-) percentiles of bootstrapped test statistics</td><td>Recursive expanding Wald test for OILANDGAS_RS G-caused by CU, 2014m1 - 2 with 90th (-) and 95th (-) percentiles of bootstrapped test statistics</td></tr></table>
<figure><img src="image_13.png" /><figcaption>**Fig. 6.** Time-varying causality from CPU and CU to realized kurtosis (RK) accounting for EPU</figcaption></figure>

Notes: Chinese climate policy uncertainty (CPU). Chinese climate uncertainty (CU). Realized kurtosis (RK). Upper and lower dashed lines represent 5% and 10% bootstrapped critical values. The size of the window is 30 months.
Panel B: Recursive expanding

<figure><img src="image_14.png" /><figcaption>Fig. 6. (continued).</figcaption></figure>
Fig. 3 Panel B presents the results derived from a recursive evolving Granger causality assessment focusing on RK. It is evident that the causal influence stemming from both CPU and CU to the RK of carbon emission allowance prices is statistically significant at the 10% level throughout the sample period. The causal flow from CPU to the RK of the Chinese green energy stock index is significant at the 10% level during the second half of 2016, the initial half of 2017, the latter half of 2020, and the initial months of 2021. Similarly, the causality from CU to the RK of the Chinese green energy stock index is significant at the 10% level during the first half of 2019 and the initial months of 2022. Furthermore, the causality from CPU to the RK of the Chinese gas and oil stock index is statistically significant at the 10% level during the last months of 2016, the middle of 2017, the years 2018 and 2019, as well as the initial months of 2021 and the initial months of 2022. The causal impact of CU on the RK of the Chinese gas and oil stock index is significant at the 5% level during the years 2016, 2017, and 2018, as well as the latter half of 2019 and the initial months of 2021.

The above findings underscore the consistent and notable causal relationships between both CU and CPU and the RK of the indices under study, with impacts that span various time periods.

The robustness of our findings is underscored by the consistency observed between the outcomes of both the rolling window and recursive evolving Granger causality tests. The convergence of evidence from these complementary approaches provides a strong basis for establishing the credibility of the causal relationships identified in our analysis.

## 4.2. Causal flow from climate risk to the realized moments after accounting for Chinese economic policy uncertainty

We extend the time-varying Granger causality analysis by accounting for the role of Chinese economic policy uncertainty (CNEPU)<fnref n="10" /> on the impact of climate risk on the energy markets, which helps us assess the robustness of the above results. This is relevant given evidence from previous studies that CNEPU matters for the price formation of Chinese assets. For example, CNEPU is exogenous to Chinese financial markets and market uncertainty, including energy assets, and can be endogenous to other factors that affect Chinese stock market uncertainty, such as corporate profit and investor behaviour. In this regard, CNEPU affects economic expectations and investor behaviours and is related to high Chinese stock market volatility (Huang & Luk, 2020). High levels of CNEPU often lead to a decrease in the estimation of future cash flows of Chinese firms and at the same time to an increase in the level of interest, both of which can generally lead to lower stock prices. Furthermore, economic policy uncertainty covers various uncertainties such as monetary, trade, fiscal, regulatory, sovereign debt, and currency uncertainty, which could affect climate policies and possibly their uncertainty.

The estimated results are reported in Figs. 4–6. In Fig. 4 Panel A, we focus on the results of the rolling window Granger causality analysis involving RV. Notably, the causal flow from CPU and CU to the RV of carbon emission allowance prices is statistically significant at the 10% level throughout almost the entire sample period.

The causality running from CPU to the RV of the Chinese green energy stock index is statistically significant at the 10% level during the initial half of 2016, the middle and end of 2018, the initial half of 2020, and the initial half of 2021. Similarly, causality from CU to the RV of the Chinese green energy stock index is significant at the 10% level during the latter half of 2016 and the year 2018. Moreover, the causality from CPU to the RV of the Chinese gas and oil stock index is statistically significant at the 10% level during the year 2016 and the initial half of 2020. Likewise, the causal flow from CU to the RV of the Chinese gas and oil stock index is significant at the 10% level throughout almost the entire sample period.

Fig. 4 Panel B shows the results of the recursive evolving Granger causality analysis involving RV. The causality running from CPU and CU to the RV of carbon emission allowance prices achieves statistical significance at the 10% level throughout almost the entire sample period. Additionally, the causal relationship originating from CPU to the RV of the Chinese green energy stock index attains statistical significance at the 10% level during the year 2016, the middle of 2017, and almost the entire period from 2018 to 2021. Similarly, the causality from CU to the RV of the Chinese green energy stock index is significant at the 10% level during the latter half of 2016, the year 2018, and the initial months of 2021. The causality from CPU to the RV of the Chinese gas and oil stock index remains statistically significant at the 10% level from 2016 until the middle of 2021. Likewise, the causal flow from CU to the RV of the Chinese gas and oil stock index maintains significance at the 10% level throughout almost the entire sample period.

Fig. 5 Panel A shows the results of the rolling window Granger causality examination, focusing on the causal flow from CU and CPU to RS. Notably, the causality from CPU and CU to the RS of carbon emission allowance prices is statistically significant at the 10% level throughout almost the entire sample period. Causality from CPU to the RS of the Chinese green energy stock index achieves statistical significance at the 5% level during the year 2016 and the initial month of 2017, as well as the initial month of 2021. Similarly, the causality from CU to the RS of the Chinese green energy stock index is significant at the 10% level around the middle of 2019 and the initial months of 2022. Moreover, while no visible causality runs from CPU to the RS of the Chinese gas and oil stock index, a notable causality is evidenced from CU. Specifically, the causal flow from CU to the RS of the Chinese gas and oil stock index is statistically significant at the 10% level during the second half of 2016.

Fig. 5 Panel B shows the results of the recursive evolving Granger causality analysis. Similar to the rolling window results, the causal flow from CPU and CU to the RS of carbon emission allowance prices attains statistical significance at the 10% level throughout almost the entire sample period. Causality from CPU to the RS of the Chinese green energy stock index is statistically significant at the 10% level during the year 2016 and the initial month of 2017, as well as the initial month of 2021. Likewise, the causal flow from CU to the RS of the Chinese green energy stock index is significant at the 5% level around the middle of 2019 and the initial months of 2022.

<footnote n="10">¹⁰ We use the monthly CNEPU index proposed by Huang and Luk (2020).</footnote>
No significant causality is detected from CPU to the RS of the Chinese gas and oil stock index. However, the causal flow from CU is significant. Specifically, causality from CU to the RS of the Chinese gas and oil stock index is statistically significant at the 10% level during the second half of 2016 and the initial month of 2017, mirroring the findings observed in the rolling window analysis.

Fig. 6 Panel A illustrates the results of the rolling window Granger causality analysis, considering the causal flow from CU and CPU to RK. It is evident that causality from CPU and the CU to the RK of carbon emission allowance prices is statistically significant at the 10% level throughout almost the entire sample period. Furthermore, causality from CPU to the RS of the Chinese green energy stock index achieves statistical significance at the 10% level during the initial months of 2017, the latter half of 2020, and the initial months of 2021. Similarly, causality from CU is significant at the 5% level during the second half of 2021 and the initial half of 2022 with respect to the RK of the Chinese green energy stock index. The causal flows from CPU and CU to the RK of the Chinese gas and oil stock index attain statistical significance at the 10% level throughout almost the entire sample period.

Fig. 6 Panel B shows the recursive evolving Granger causality analysis involving RK. Similar to the rolling window results, the causal flow from CPU and CU to the RK of carbon emission allowance prices attains statistical significance at the 10% level throughout almost the entire sample period. The causal flow from CPU to the RK of the Chinese green energy stock index achieves statistical significance at the 10% level during the initial months of 2017, the latter half of 2020, and the initial months of 2021. Similarly, causality from CU to the RK of the Chinese green energy stock index is significant at the 5% level during the second half of 2021 and the initial half of 2022. Likewise, the causal flows from both CPU and CU to the realized kurtosis of the Chinese gas and oil stock index attain statistical significance at the 10% level throughout almost the entire sample period.

Taken together, we note a coherence between the results of the rolling window and recursive evolving tests, which emphasizes the robustness of our findings to the specification of the time-varying analysis, but also shows that most of the causality running from CPU and CU to the higher-order moments of the three Chinese assets remains statistically significant over several time periods, accounting for the role of Chinese EPU. This reinforces the credibility, robustness, and validity of the causal relationships identified in our above analysis. Accordingly, the causal flow from climate risks to the higher-moments of assets returns in China is not subsumed by uncertainties regarding economic policy but is mainly of a climate policy nature.

### 4.3. Discussion

The present study examines the intricate and dynamic interplay between each of CPU and CU and the realized high-order moments - volatility, skewness, and kurtosis - of diverse Chinese assets encompassing green energy, carbon emission allowances, and brown energy indices. Employing both rolling window and recursive evolving Granger causality tests, our analysis yields insightful and consistent findings that shed new light on the temporal dimensions of these relationships.

Our results reveal a noteworthy pattern of Granger causality, with both CPU and CU demonstrating statistically significant causal impacts on the realized volatility of carbon emission allowance prices. A similar robust causality pattern emerges for the realized skewness and kurtosis of carbon emission allowance prices, where the influence from both CPU and CU remains prevalent over time.

For Chinese green energy and brown energy stock indices, our findings demonstrate complex temporal variations in causal flows. The causal pathways exhibits a sensitivity to distinct periods, with CPU and CU displaying significant influences on realized volatility, skewness, and kurtosis. Interestingly, the robustness of the causal effects across both rolling window and recursive evolving analyses reinforces the credibility of the established causal relationships at the higher-order moments of Chinese assets. Furthermore, the significance of causal flows remains statistically significant when taking into account the effect of the Chinese EPU on the causality relationships.

Overall, our study contributes to the emerging literature on climate policy uncertainty by offering nuanced insights into its effects on the high-order moments of various Chinese assets. The dynamic nature of these causal relationships highlights the importance of accounting for temporal dynamics when assessing the impact of climate uncertainty on asset markets. The findings of this study have implications for investors, policymakers, and risk managers seeking to better understand the multifaceted interactions between climate uncertainties and asset price dynamics. In China, the impact of climate change and uncertainty are crucial, because China is one of the biggest producers of natural resources. It relies on its moderate environmental standards and low-cost labour to capture market share and gain a dominant position in global markets. At the same time, the Chinese government supports the development of a competitive upstream industry.

## 5. Conclusion

Unlike existing studies dealing with the impact of Chinese climate risk on the returns and volatility of Chinese financial assets, in this paper we focus on the impact of Chinese climate risk on the higher-order moments (volatility, skewness, and kurtosis) of three Chinese assets covering green energy, carbon emission allowances, and brown energy indices. Using CPU and CU, and applying a time-varying Granger causality test, we show evidence of a time-variation in the causality from both Chinese climate risks to the realized volatility of the three Chinese assets. Causality is more significant and pronounced in realized volatility, without underestimating the causal flow in the realized skewness and kurtosis. Furthermore, there is a heterogeneity in the causality between the two climate risk
measures, which reflects the difference in their construction and reflection of physical and transitional climate risks. Finally, we show that our results remain significant when accounting for the impact of Chinese economic policy uncertainty, which indicates that the detected significant results of causality are credible and robust.

Our findings have several implications for Chinese policymakers and, given the energy transition towards cleaner energy and the efforts made to reduce the high level of greenhouse gases in China, policymakers and governments should pay attention to both physical and transitional climate risk and their significant impact on the volatility, skewness, and kurtosis of Chinese clean-and non-clean energy assets. Eventually, decision makers in China may benefit from the above findings on the importance of climate risks for volatility, skewness, and kurtosis of Chinese assets when designing and formulating comprehensive policies for the sake of market stability and the smooth transition to sustainability. Investors, especially environmentally conscious investors, should pay attention to the impact of climate risks on skewness and kurtosis when making investment and risk management decisions beyond the first and second moments of returns. This is relevant for asset allocation and portfolio optimization, and thus the risk management inferences involving higher-order moments. This should be used to extend the econometric understanding of the response of higher-order moments of Chinese asset returns under climate risk and how portfolios can be constructed to cope with such a significant causality involving not only volatility but also downside/upside and tail risks. These issues can be the subject of future studies.

## Data availability

No data was used for the research described in the article.

## Appendix

<figure><img src="image_15.png" /><figcaption>Fig. A.1. Plot of CPU and CU indices over the full sample period (December 2013–June 2022)</figcaption></figure>
<figure><img src="image_16.png" /><figcaption>Fig. A.2. Plot of realized higher-order moments of CEN, CEA, and BEN over the full sample period (December 2013–June 2022)</figcaption></figure>
<figure><img src="image_17.png" /><figcaption>Fig. A.3. Returns of CEN, CEA, and BEN over the full sample period (December 2013–June 2022)</figcaption></figure>

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