1. Introduction
As a critical energy carrier, electricity is an important material basis for a nation’s social and economic development and prosperity. However, over the past decades, many countries have suffered from electricity shortages due to the non-storage nature of electricity and the frequent occurrence of severe emergencies, especially extreme weather events. It means that the stability of social order is seriously threatened, and the electricity market is also full of uncertainty, resulting in greater volatility in electricity prices compared with other similar commodities [
1]. More importantly, the virtual economy is inextricably linked to the real economy, and either side will inevitably be affected by the fluctuations of the other side. Therefore, these events can also affect the investment confidence of individual and institutional investors, and exacerbate the volatility of the financial market, leading to an intensified investor panic.
China’s electricity industry has experienced rapid development in recent years. So far, it is the largest country in the world’s electric supply and electric demand. To narrow the gap between the rapid growth in electricity demand and supply, and ensure the electricity sector structure fit in with China’s economic construction and social development, the government launched a series of reforms. With the steady progress of these reforms, the construction and development of China’s electricity market have made some obvious progress [
2,
3,
4,
5,
6,
7,
8], but the remaining problems have likewise drawn widespread attention from scholars. Zheng et al. [
5] find that the electricity reform in March 2015 significantly improves technical efficiency but reduces the reliability of the electricity supply. Similar findings are also reported by Gohli [
9] that China is suffering from electricity shortage under the combined impact of the energy crisis and economic recovery, and electricity generators are also under considerable strain in terms of both generation costs and electricity supply. In addition, reforms to liberalize electricity prices have exacerbated electricity price volatility [
10] and increased uncertainty in the electricity market. Therefore, it is necessary for investors to choose hedging commodities to offset their investment risks and protect their profits.
With the rising levels of financial integration and liberalization, strong risk contagion and huge price fluctuations have been observed frequently in financial markets, leading to lower portfolio returns but higher risks [
11]. As a result, to investment risk without reducing expected returns, investors increase their research interest in commodity markets to re-examine the diversification strategy of the asset portfolio and turn to include some specific commodities in their asset allocation, most often gold and oil [
12].
Gold and oil are the most actively traded commodities in the world and are often used by investors to hedge their investment risks [
13]. Historically, gold has always been regarded as a flight-to-safety asset, as its inflation-proof properties have been generally accepted [
14]. Furthermore, gold can also resist the risks associated with some major uncertain events or emergencies [
15], especially since the performance of gold is impressive during the stock market crash. On the other hand, as an important strategic material for industrial production and economic development, oil is also one of the strong hedging instruments that can be served as a safe haven for investors. In particular, in some specific sectors or emerging markets, oil displays a superior hedging performance than gold. Basher and Sadorsky [
16] find that the hedging effectiveness of oil is significantly higher than those of gold, VIX, and bonds in hedging against emerging market stock prices. Xu et al. [
17] observe that oil has the largest hedging capacity on the Chinese stock market among the four economies of the United States, Japan, China, and Hong Kong. Kang et al. [
18] find that even though the cost of hedging with gold is similar to that of oil in the short term, oil has stronger hedging effectiveness on sector ETFs than gold in both the short and long term. In addition, Naeem et al. [
19] claim that oil is a better hedging tool than gold for industrial metals and agricultural commodities. Therefore, in this paper, we choose crude oil and gold as hedging instruments for electrical companies.
Based on the background mentioned above, this paper focuses on the research of volatility spillover effects between commodity markets and Chinese electricity stock markets. We select some stocks from the main board of China’s A-shares as representatives of the Chinese electricity industry stock market; meanwhile, oil and gold are chosen as hedging and portfolio diversification tools. To examine the time-varying correlations in the volatilities of crude oil, gold, and electricity companies’ stock prices, we approach the dynamic conditional correlation (DCC) model proposed by Engle [
20], which estimators have the flexibility of univariate GARCH but not the complexity of conventional multivariate GARCH. It should be noted that we can use the DCC model to get the correlation between two return series over time, but it does not provide any information on how the volatility of one series is affected by the volatility of the other [
21]. In recent years, Diebold and Yilmaz [
22] proposed the measurement method of overall and directional volatility spillover under the framework of generalized vector autoregression, which has been widely used in the study of the volatility spillover effect. However, the method suffers from window loss due to the limitation of the scrolling window. In addition, short scrolling window length will stop the estimation, and too much will lead to overfitting [
23]. Therefore, we use the time-varying parameter VAR model (TVP-VAR) proposed by Antonakakis et al. [
24] to examine the volatility spillover effects. This model is a generalized version of the volatility spillover approach of Diebold and Yilmaz [
22]. In particular, the main advantages of the TVP-VAR model are not affected by the size of the rolling window and do not lose observations [
25]. Moreover, the presence of an outlier would not significantly affect our results, which provides a better adjustment to parameter changes [
26]. Then, we calculate the optimal hedge ratios and portfolio weights, as well as the corresponding hedging effectiveness. More importantly, to illustrate the effects of different crises, we compare the properties of hedging and safe-haven against risk during two sub-periods.
Overall, the contributions of this study to the related literature can be summarized as follows. First, we analyze the volatility relationship between China’s electricity stock market and the commodity market. Although the development of China’s electricity market has attracted much attention recently, to our best knowledge, few papers have analyzed China’s electricity industry from the perspective of investment. Even more, the literature on the interaction between China’s stock market and commodity markets has mostly focused on aggregate analysis, which may mask the heterogeneity between different industries and is not very useful for analyzing diversified portfolio strategies. Thus, this paper fills these gaps in the literature by investigating the volatility spillover effects among oil, gold, and the stocks of major electricity companies in China. Second, we extend the literature in the area of spillovers between China’s stock market and commodity markets, particularly under different market conditions. Specifically, we examine the time-varying correlation and spillover effects in the two crisis periods, the 2015 Chinese stock market crash and COVID-19 in 2019, and illustrate that the impact of the crisis varies depending on the feature of the crisis and the characteristics of the series analyzed. Third, our research quantifies the hedge ratio, optimal weights, and the hedging effectiveness of the electricity stock-commodity portfolio, which can help portfolio managers adjust optimal hedging and portfolio diversification strategies to make more favorable decisions, especially during such crisis periods. Additionally, these main findings also provide a reference for policymakers to quickly formulate measures to deal with external emergencies in the process of China’s electricity market reform.
The remainder of this paper is organized as follows.
Section 2 briefly reviews the relevant literature.
Section 3 lays out the methodology employed in this paper.
Section 4 consists of the dataset employed and descriptive statistics, and the most significant empirical results and discussion are reported in
Section 5. Finally, in
Section 6, we provide some concluding remarks.
2. Literature Review
Electricity is an indispensable part of economic growth. However, although lots of studies are related to electricity, only in recent years have scholars paid attention to the volatility spillovers of electricity markets. Souhir et al. [
27] point out that electricity prices are more volatile than other commodity prices due to the liberalization of electricity markets. Interestingly, similar results are also reported by Han et al. [
1]. They examine the volatility spillovers among five regional electricity markets in the NEM and conclude that the volatility of electricity markets is significantly higher than that of other comparable financial or commodity markets, and the pattern of spillover effects may be influenced by the market structures and specific events. Based on the connectedness measure originally proposed by Diebold and Yilmaz [
28], Ji et al. [
29] found that electricity prices are the biggest information receiver among the energy and carbon markets. Furthermore, from the perspective of the firm level, the outcomes of Ji et al. [
30] show that the carbon market serves as a recipient in the information spillover among carbon price returns and European electricity returns, and large companies often contribute more information to the system compared to small companies. In addition, Naeem et al. [
31] find that because of the low short-term correlation between electricity futures and other variables, it can be used as a risk-diversification tool and a safe haven asset in the portfolio, especially against the risk of oil shocks. Liu et al. [
32] provide more information on the spillover effects of electricity spot and futures markets. They observe that the spillover effect of natural gas on the electricity market is the highest in the European fossil fuel market. This result is not in line with the findings of Zhang et al. [
33], where the authors find that both in North America and in Europe, oil has a stronger volatility spillover on electricity utility stock indices than natural gas. Moreover, they confirm that extreme events can significantly affect the spillover effects.
With the acceleration of rapid economic growth and modernization, the Chinese government has taken a series of measures to support the fast-growing demand for electricity, which has attracted considerable attention from academics. Nevertheless, the focus of attention is either on the achievements or impacts of these measures or on the problems that still exist in the electricity market. For example, Guo et al. [
34] review a series of reforms launched by the Chinese government and focus on the new round of electricity sector reform in terms of motivations, progress, and recommendations. Their investigation reveals that China’s electricity market reforms achieve some progress in reducing pollution, increasing generation efficiency, developing renewable energy, and so on. The steadily increasing marketization of China’s electricity price is also confirmed by Zhao and Hu [
35], and they also observe that marketization can effectively motivate the promotion effect of electricity price on energy efficiency. Zheng et al. [
5] report similar results in energy efficiency, but they also find a decrease in the supply reliability of electricity. In addition, some authors examine the impact of the development of China’s electricity sector on renewable energy [
2,
3,
8] and carbon emission [
4,
6,
7]. However, only a few papers specifically analyze China’s electricity market from the view of investment [
36,
37].
It is well acknowledged that risk is the focus of attention from investors and policymakers. Considering that the contagion effect within the stock market is increasing with financial liberalization, while the correlation between the commodity market and the stock market is relatively low, more and more investors put commodities into their portfolio strategies to hedge their risk without decreasing the expected returns. However, it is important to note that the volatility spillover effect means risk contagion, which can weaken the ability of asset portfolios to reduce or avoid risks [
23]. Therefore, a large number of studies have examined and emphasized the volatility spillover effect and the portfolio diversification of commodity markets and stock markets, but most of them focus on aggregate stock market indices [
24]. For example, Vardar et al. [
38] find that the volatility spillover effects among ten countries’ stock market indices and the spot price of five major commodities evolve depending on the market conditions. Ali et al. [
39] reveal 21 commodities’ hedge, safe haven, and diversification potential for 49 international stock markets. In addition, they corroborate that gold is the strongest haven asset, especially for some developed stock markets. Wen et al. [
23] observe that there is a highly dependent relationship between the Chinese stock market and Chinese commodity markets, and the level of total volatility spillover varies in different periods. Ahmed and Huo [
40] suggest that there is a significant unidirectional return spillover effect from the oil market to the Chinese stock market, while there is no return spillover between gold and these two markets. Reboredo et al. [
41] investigate connectedness spillovers among six stock indices and three blocks of markets. They believe that the risk effect of stock markets needs to be hedged with other asset classes or financial derivatives.
Considering that aggregate stock market indices may hardly reflect the volatility spillover effects between various sectors and the commodity markets [
24,
42], some scholars have analyzed the relationship between commodity assets and stocks at the sectorial or company level in recent times. For example, Morema and Bonga-Bonga [
42] simultaneously evaluate the impact of gold and oil price fluctuation on index volatility of the aggregate stock market and three component sectors. They conclude that both gold and oil are powerful hedging tools, but oil can only be used to mitigate risk in the industrial sector, rather than the risk in the aggregate stock market. Dutta et al. [
15] discern that volatility indices of crude oil, gold, and silver are all effective tools to reduce the risk of clean energy stock indices. In addition, Mensi et al. [
12] demonstrate that sectoral indices of the stock market react differently to the variabilities in commodity prices. For the firm level, Antonakakis et al. [
24] observe that there are significant volatility spillover effects among crude oil and oil and gas companies’ stock volatility, and the spillover is usually unidirectional. Maitra et al. [
43] examine the volatility co-movement and spillover between oil and liner shipping stocks and confirm the importance of oil as a hedging instrument. Further, the outcomes suggest there are persistent volatility spillovers between oil prices and 22 international transportation or logistic companies’ stock returns, and maximizing risk-adjusted gains can be achieved by adjusting the portfolio according to the economic situation.
The studies mentioned above show that most research has been dedicated to the hedging and safe-haven properties of commodity assets against the stock market. But undeniably, the results are not consistent, potentially because they focus not only on different sectorial indices but also on different periods. In particular, the spillover effects between commodity assets and stock markets are susceptible to some major crisis events, such as the financial crisis and the COVID-19 pandemic [
44]. The results of Wen et al. [
23] suggest that the volatility correlations between commodity markets and stock markets increase following major crisis events, and the risk-hedging ability of most commodities decreases significantly. Maitra et al. [
43] evaluate how the global financial crisis in 2008 affects the spillover dynamics between oil and liner shipping markets. They elaborate on the importance of choosing appropriate strategies in different economic environments. Unlike the financial crisis, the recent outbreak of COVID-19 has not only brought enormous risks to the global financial market and economy but also has immensely impacted the social and environmental pillars of sustainability in human lives [
45,
46,
47]. To contain this highly contagious “once-in-a-century” pandemic [
48], governments around the world had to take extreme steps like lockdowns, social distancing, and suspending business operations. These measures have led to a sharp decline in electricity demand [
49] and also slowed the transition to a sustainable energy world [
50]. Additionally, the relationship between commodity and stock markets has also changed significantly by COVID-19 [
51]. Zhang et al. [
52] observe that during the trade disputes between China and the United States and the COVID-19 pandemic, the spillover effects between commodity and stock markets intensify. Dai et al. [
53] confirm that the volatility spillovers among WTI crude oil, gold, and the Chinese stock markets of new energy vehicles will be affected by major events such as the oil price crisis and COVID-19.
Along with the prosperity of China’s economic construction and the rapid development of the stock market, researchers have paid more attention to analyzing the nexus between the commodity and Chinese stock markets [
54]. For example, Bouri et al. [
55] compare the causality-in-mean and causality-in-variance between the international oil market and the Chinese stock market before and after China’s refined oil pricing reform of 27 March 2013. They find that the causality-in-mean between the two markets strengthened after the reform, whereas the causality-in-variance almost disappeared. Dai et al. [
53] confirm that crude oil and gold are cheap hedging tools, and they can also be used as diversifier assets to reduce the risk of Chinese energy vehicle stock indices. Similar results are also reported by Mensi et al. [
56], who demonstrate that the inclusion of oil and gold in China’s equity portfolio can reduce the level of risk. Furthermore, they also observe that gold provides better hedging effectiveness than oil in both tranquil and turbulent periods. In addition, the results of Wang and Li [
57] support that gold can serve as a good risk-hedging tool in the short term. Conversely, Peng [
58] finds that gold only acts as an effective diversifier for China’s stock, and it performs well as a safe haven against stock market risk during the financial crisis. The findings of Zhang et al. [
59] are consistent with those of Peng [
58]. They perceive that gold is a good diversifier rather than a hedging tool for China’s stock market, and it should be preferred over oil and Chinese stocks in portfolios. However, the results of Bouri et al. [
60] show that the safe-haven property of gold is not stable when they examine the short-term and long-term causality dynamics between gold and the Chinese and Indian stock markets using implied volatility indices. Additionally, Hsiao et al. [
61] find that oil has a significant spillover effect on China’s renewable energy companies’ stock prices. In contrast, the research of Qu et al. [
62] indicates that the volatility of oil only causes short-lived shocks to new energy stocks, indicating that it is not the dominant factor for the volatility spillover and investment risk of new energy stocks in China.
In summary, the achievements and shortcomings of China’s electricity industry development have been widely discussed in recent years, but few papers put their attention to the electricity stock market from an investment perspective. Thus, it is of great significance for policymakers and investors to analyze the volatility spillover and investment risk in the electricity stock market, especially in the extreme risk period, such as the COVID-19 crisis. In addition, lots of literature have already analyzed the correlation and volatility spillovers between commodity markets and stock markets, most of this literature focuses on aggregate or sectorial indices. However, it is difficult for aggregate and sector indices to reflect the heterogeneity at the firm level. While diversification strategies and risk management mainly involve specific companies, implying that portfolio diversification and risk management at the firm level is more useful to investors than aggregate or sectorial analysis. Based on the analysis above, our research aims to investigate the volatility spillover effects between electricity companies and two major commodities, oil and gold, to illustrate the volatility contagion and implement optimal diversification strategies, especially in turbulent times like the COVID-19 pandemic.
4. Data
In this paper, we utilize daily data on crude oil prices, gold, and Chinese electricity companies’ stock prices. It should be noted that, we use time series data, which better reflects the macro situation [
67,
68,
69,
70] and is more suitable than panel data [
3,
71]. Crude oil prices are measured by the continuous contract on the West Texas Intermediate (WTI) crude oil futures contract, and data are obtained from U.S. Energy Information Administration (EIA). This paper only chooses 12 representative electricity companies from China’s A-share main board due to the limited availability of data (serious missing data or too short a time for the company to be listed). In order of assets from largest to smallest, the selected corporations are the following: Huaneng Power International incorporated (HPI), Datang International Power Generation Corporation Limited (DTP), Huadian Power International Corporation Limited (HDPI), Shenergy Company Limited (SNGY), Guangzhou Development Group Incorporated (GDG), Sichuan Chuantou Energy Corporation Limited (SCTE), Changyuan Electric Power Company Limited (CYDL), Guangxi Guidong Electric Power Corporation Limited (GDEP), Chongqing Three Gorges Water Conservancy and Electric Power Company Limited (CQSXSL), Xingjiang Tianfu Energy Corporation Limited (TFNY), Guizhou Qianyyuan Power Company Limited (GZQYDL), and Hunan Chendian International Development Corporation Limited (CDGJ). It should be noted that because China has relied largely on coal and water resources to generate electricity for a long time [
72], we select only seven thermal power companies and five hydropower companies, and these companies are represented in terms of generation and stock market value. For example, HPI has the largest market value and the installed capacity of power generation among thermal power generation companies, while HDPI and DTP also rank third and fourth, respectively. As for hydropower, SCTE is also in the front rank of installed capacity and market value. In addition, some small companies also have been included in this paper, such as TFNY and CDGJ have small market value, especially CDFJ, which is the smallest hydropower company in terms of market capitalization. The stock prices of the companies mentioned above and the gold price on the Shanghai Gold Exchange (GOLD) are collected from the CSMAR database.
It is important to highlight that our sample covers recent major crises attacking China. The whole sample period extends from 2 December 2008 to 25 July 2022 with 2920 observations. It should be noted that we chose to start our estimation sample from the end of 2008 because, in addition to the 2015 stock market crash, China’s stock market also experienced a crash in 2008 and stabilized at the end of 2008. The highest point of the Shanghai Stock Exchange Composite Index (SSECI) in the 2008 crash was 6124.04 on 16 October 2007 and the lowest point was 1664.92 on 28 October 2008 [
73]. Furthermore, we implement analysis for two sub-periods: the stock market crash and the COVID-19 epidemic periods. The first sub-period is China’s stock market crash range from 15 June 2015 to 27 January 2016. During this period, the SSECI lost more than 49% from 5176.79 to 2638.3. The plunge did not stop until the end of the following January. Another sub-period was set from 31 December 2019 to 8 September 2020, which includes the major periods of the spread of COVID-19 in China. Concretely, the WHO China office officially announced the first infected case of pneumonia in Wuhan on 31 December 2019, while the national commendation conference for fighting COVID-19 in China was held on 8 September 2020, which marked the beginning and normalization of the COVID-19 epidemic [
74], respectively.
Following Antonakakis et al. [
24], the price volatility is defined by taking the absolute return of each analyzed variable, as shown in Equation (1), where
Pt denotes the daily closing value of crude oil prices and stocks at the time
t.
The volatility statistics for WTI, gold, and stock prices of major Chinese electricity companies are shown in
Table 1 and several notable points can be obtained. Firstly, the results demonstrate that WTI is the most volatile and GOLD is the least, with a value of 3.281 and 0.729, respectively, while the standard deviation of other series is between 1.5 and 2.5. Secondly, the skewness coefficients and the kurtosis coefficients indicate that the distributions of all volatility time series are right-skewed and peaked. We examine the existence of the ARCH effect using the Lagrange multiplier test proposed by Engle [
75], and the results show the obvious ARCH effect of all returns. Additionally, the results of Jarque-bera statistics suggest that each series is far from normally distributed. A final observation that is worth mentioning here is the results of the ADF test indicate that all sample series are stationary processes.
Table 2 reports the Pearson (above diagonal) and Spearman (below diagonal) correlations. It shows the unconditional correlations among volatility of the analyzed series. Both WTI and gold have obvious positive correlations with the volatility of the analyzed electricity companies and correlations are weak, but the correlation of gold is weaker than WTI. Additionally, a high correlation can be found among the electricity companies. Then, diversification is of great significance to reduce investment risk.
6. Conclusions
This paper estimates the volatility spillover effects and dynamic relationship among WTI, gold, and the Chinese electricity stock market from the perspective of the firm level. We calculate the optimal hedge ratios and portfolio weights between the analyzed commodities and electricity companies’ stocks, as well as the hedging effectiveness. In particular, this paper also highlights the implications of the crisis on the volatility spillover and diversification strategies. We further analyze the spillover effects in two sub-periods, including China’s stock market crash period and the COVID-19 period.
The main findings of this paper can be concluded as follows. First of all, a time-varying correlation between the volatility of commodity assets (including crude oil and gold) and the analyzed electricity companies’ stock prices can be observed. The correlations are not high in normal circumstances, indicating portfolio diversification is feasible. In addition, unlike WTI, although the correlations between gold and the stock prices of these companies are also vulnerable to extreme events such as the COVID-19 pandemic, the correlations are relatively weak and show different trends during the crash of China’s stock market.
Secondly, by comparing the static volatility spillovers in different periods, the preliminary result suggests that the crisis tends to increase total spillover effects, which is also confirmed by the results of the dynamic time-varying spillover index. Additionally, even though WTI and gold are net volatility receivers both throughout the whole period and during the crisis, it is notable that the impact of the crisis on spillover effects varies due to the feature of the crisis and the characteristics of the analyzed series. Concretely, volatility spillovers for both commodity assets and electricity companies’ stocks increased significantly in the period of COVID-19, while in the stock market crash period, only electricity companies’ stocks increase the intensity of volatility spillover transmitted to WTI. More interestingly, we find that larger electricity companies tend to be net transmitters excepting the HPI, and some small electricity companies transmit more volatility spillover to WTI in the market crash period.
Thirdly, we calculate the optimal hedge ratios and the hedging effectiveness in different periods. The values of optimal hedge ratios are relatively low when WTI is taken as a short position, except during periods of stock market crashes. However, most values of hedging effectiveness of WTI hedging against electricity companies’ stocks are negative, especially during the COVID-19 pandemic, indicating that the strategy of WTI served as a short position would not be preferred. Gold is a much cheaper hedging tool compared with WTI, and the hedge ratios are generally lower when investors take a long position in the volatility of gold.
Finally, we assess the optimal portfolio weights and the results show that the average weights for gold in the optimal portfolio always exceed 50%, reflecting the investors’ preference for gold. In addition, compared to the whole period, the optimal weights of gold increased during the stock market crash period and declined during the COVID-19 period. Notably, the results for WTI are similar, but the hedging effectiveness of the WTI-stock pair is generally higher compared to the gold-stock pair. Furthermore, from the perspective of hedging effectiveness, most optimal weight strategies provide better hedging effectiveness compared to traditional hedging strategies.
Overall, our findings offer some recommendations for international investors and policymakers. The diversification strategy consisting of commodity assets and stocks can provide opportunities for international investors to reduce investment risk without reducing profits. It is worth emphasizing that while many investors prefer gold because of its safe-haven properties, gold is not always the best hedging tool, and other commodities such as oil may have better hedging effectiveness in certain circumstances. Moreover, although on average large companies have a greater impact on the market, it does not mean that they are suitable for inclusion in the portfolio strategy. In other words, investors need to dynamically balance their investment strategies with the characteristics of the commodity-stock portfolio and the current economic situation, especially carefully analyzing the underlying causes of market turbulence. As for policymakers, they should monitor the volatility spillover effects between commodity assets and stock markets, and be aware that such spillover effects can be exacerbated by the crisis. Therefore, they should not only guard against the impact of the crisis on the financial markets, but also formulate policies based on the characteristics of the crisis and the source (direction) of the volatility spillover, and implement the energy transition as soon as possible while meeting the country’s growing energy needs.
There are some limitations of this study. First, although this paper has tried to expand the scope of the study as much as possible, due to objective reasons such as the availability of data, some new energy generation companies could not be included in the scope of the study. Therefore, future research could focus on this aspect. Second, this paper only makes a comparative analysis of the sub-samples of China’s stock market crash in 2015 and COVID-19 in 2019. Future research can make further comparisons to more crises or landmark events. Moreover, this paper only analyzes the volatility spillover effects between China’s electricity stock market and the commodity market. In further studies, we hope to expand to other markets and analyze whether these crises have a different impact on other countries. Finally, to provide more useful information for investors with different investment horizons and different levels of risk tolerance, future research can also consider decomposing the spillover effects by frequency.