مطالعه ای بر روی اثر سرریز نوسانات، اثر بلند مدت و تعامل بین کربن و بازارهای انرژی: اثرات آب و هوایی شدید

Due to the connections of energy uses, carbon emissions and climate, this study investigates the interactions, volatility spillovers, and long memory effects for carbon, oil, natural gas and coal markets by using FIEC-HYGARCH model. It also discusses the mediating effect of extreme weather. The empirical results verify that the FIEC-HYGARCH model can capture the long-term volatility behavior. The futures returns of carbon and energy have long memory and own-mean spillover effects. Moreover, the conditional variances also have volatility spillovers, long memory effects and amplitudes. Hence, there exist dynamic interrelationships among the futures returns of carbon and energy. Further, it also extends the long memory and causes various spillover effects by incorporating extreme weather into the model, indicating that extreme weather has certain impacts on carbon, oil, natural gas and coal markets.

The problem of CO2 emissions from fossil fuel uses (oil, natural gas, and coal), has caused a great deal of concerns around the world. In order to control carbon dioxide emissions, the carbon market has developed since 2005 based on the mechanisms of the Kyoto Protocol. The carbon price is affected by carbon emissions, which implies that energy prices have certain impacts on it. Moreover, information shocks may lead to any fluctuations in these markets, thus the information transmission and spillover effect cannot be ignored. In general, the prices are not reflected immediately by news or a shock, which implies that past long-lagged prices may still have strong influences on today's prices (i.e. long memory). Furthermore, abnormal weather leads to more energy uses and higher CO2 emissions, which may reflect on carbon and energy prices. Therefore, this study tries to investigate the interactions, volatility spillovers, and long memory effects between carbon and energy markets by using FIEC-HYGARCH model, and the impacts of extreme weather are also taken into consideration. Human beings have consumed a large amount of fossil fuels and altered the use of land to improve their economic activities since the Industrial Revolution. Moreover, other human activities, such as deforestation and burning waste, have changed the lifestyle of individuals and the environment. These human activities have caused a serious problem, greenhouse effect, which brings various impacts on the environment, such as climate change. Although these changes have made humans become richer, the environment has also been harmed severely due to the greenhouse effect. The greenhouse effect is due to the increase of greenhouse gases (GHGs) in the atmosphere, especially during the period of booming economy with higher energy uses. And the increase of greenhouse gases is believed to be the result of the preceding description about human activities. It actually has caused a severe problem, global warming. Global warming has been causing many problems, such as ice melting, rising sea levels and even climate changes. All of these phenomena are harmful to the ecosystem environment and threaten the lives of human beings. According to IPCC Fourth Assessment Report (2007), global anthropogenic GHG emissions have increased by about 70% between 1970 and 2004. Anthropogenic GHG emissions include carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs) and sulphurhexafluoride (SF6). Carbon dioxide is the most important anthropogenic GHGs due to its largest share of GHGs and significant annual growing emissions. The rising of CO2 concentration is mainly contributed by fossil fuel use, and it is the main cause of greenhouse effect and global warming. Hence, this study investigates the relationships of carbon and energy markets. Other than the above description, it is necessary to understand the formation of carbon market. In recent decades, there has been more and more concern about the issue of climate change and global warming. The United Nations Environment Programme (UNEP) and World Meteorological Organization (WMO) mutually established the Intergovernmental Panel on Climate Change (IPCC). Furthermore, the United Nations Framework Convention on Climate Change (UNFCCC) is an international treaty for member countries to reduce global warming and stabilize the concentration of GHGs. On 11 December 1997, the Kyoto Protocol was adopted by Conference of the Parties (COP-3) of UNFCCC, and entered into force on 16 February 2005. It requires 37 industrialized nations and the European community to reduce GHG emissions and meet their targets by these three market-based mechanisms. These mechanisms are Emissions Trading (ET), Clean Development Mechanism (CDM) and Joint Implementation (JI), which are offered for nations to meet their individual targets, and they encourage green investment and achieve the cost-effectiveness of emission targets. In Article 17 of the Kyoto Protocol, Annex B Parties (parties with commitments under the Kyoto Protocol) have to fulfill their obligations to meet individual emissions targets. The carbon market was created under the Kyoto Protocol in 2004, and it allows countries and businesses to trade emission units, or “assigned amount units” (AAUs). Through the carbon market, countries that have extra emission units can sell them to the countries that demand for them; those countries that lack emission units can buy them from other countries. Hence carbon becomes a new commodity and it is traded like other commodities. Carbon emissions trading has become a means for countries to reduce the amount of carbon emissions to meet their obligations and thereby mitigate global warming. In terms of global carbon market, the EU Emission Trading Scheme (EU ETS), the world's largest carbon market, was launched in 2005. The global carbon market has attracted numerous investors' attention, and the volume of emissions trading has been growing steadily in recent years. This is the main reason why we discuss the example for the relationships between carbon and energy markets in European countries. Based on the previous description, there are several motivations for this study. First of all, most literatures have showed the determinants of carbon prices, such as energy prices, weather variations, economic growth, and policy issues (Christiansen et al., 2005 and Springer, 2003). Some of them even focus more on the interactions between carbon and energy markets for the Phase I (2005–2007). Carbon price is affected by energy prices significantly due to the connection of fossil fuel uses and carbon emissions. Therefore, understanding the connection of carbon and energy markets provides the basic background knowledge for constructing the empirical model. Except to the understanding of carbon and energy markets, the market volatilities are needed to be considered. Information shocks may lead to fluctuations in one market, or even transmit its volatility to other markets. Thus the information transmission and spillover effect are needed to be considered. Furthermore, the price performance today could be affected by past long-term news or a shock (i.e. long memory). Hence, the spillover and long memory effects are one of the major concerns in this study. While most studies use regression methods or GARCH models to capture the interactions of carbon and energy markets, we hardly found the literatures that focus on the spillover and long memory effects (i.e. the current performance is affected by the past long-term behavior, and leads to a long range dependence in the time series). Therefore, we use FIEC-HYGARCH (fraction integrated error correction-hyperbolic GARCH) to analyze the interactions, spillovers and long memory effects between carbon and energy markets. Except the energy prices, weather variations are another important carbon price driver. Numerous studies have showed the effect of climate on energy and carbon prices. Both temperature increases and decreases can lead to more energy consumption and increase their CO2 emissions, hence raise the carbon prices. Therefore, we need to consider the impact of weather variables on carbon market by using extreme temperatures as dummy variables in the empirical model. This study contributes to a better understanding of the interactions of carbon and energy markets in Europe. We also try to capture the spillovers and long memory effects between these two markets whether with weather variable or not. The empirical results of this study can provide better investing information for the global and domestic investors, including asset allocation, portfolio diversification and hedging strategy. For industrial firms, this study can help them to do better decisions or hedging strategy towards the impacts under environmental policies. Besides, this study can also provide the related information of global carbon market for Taiwanese government or agencies to establish domestic carbon market in the future. The remaining parts of this study are organized as follows. Section 2 outlines the literature review. Section 3 summarizes the empirical methodology, followed by the empirical results reported in Section 4. Finally, Section 5 presents some concluding remarks.

Long memory in time series data means that current behavior is affected by past long-run behavior, reflecting a phenomenon of long-range dependency effect. We conclude that the long memory model adequately simulates the volatility of financial data, and therefore our study formulates a FIEC-HYGARCH model, incorporating a HYGARCH model into a fractionally integrated error correction (FIEC) model to analyze the interrelationships among the carbon, oil, natural gas and coal markets, and the impacts of extreme weather are also considered. The sample period of this study extends from January 1, 2008 to December 31, 2011. The empirical results have several findings as follows. Firstly, through analyzing the FIEC-HYGARCH models, the results show that there is a long memory effect, meaning that past information can be used to forecast future prices. Additionally, carbon and energy markets all have own-mean spillover effects, thus the investors and firms can use past prices to forecast the future prices, or even make appropriate strategies, such as to buy or sell the allowances of CO2 emissions. There also exists cross-spillover effects, so energy returns and extreme weather are essential for considering the carbon price changes. The results also indicate that carbon, oil, natural gas and coal returns all have long memory effects in their own markets, implying that past information can be used to forecast future volatilities. For the amplitude of volatility, carbon market has the largest volatility amplitude, and this may be resulted from its characteristic of relatively new market. To be more specific, carbon market is a relatively immature market that lacks the transparency of market information or may be easily affected by external factors, such as policy changes. Furthermore, extreme weather has significant impacts on carbon and coal markets, which may be resulted from the greater uses of heating and air-conditioning under extreme weather, leading to larger CO2 emissions. Carbon and energy markets also have covariance amplitudes and long memory effects, indicating that there exists long memory among these markets. This means that the covariance between two markets may be affected by their past events, and the returns may influence with each other and have the feature of time-varying. There are strong connections among carbon and energy markets in Europe. Investors should consider the performance of each market to diversify the investment portfolio risk and construct the advantageous investment and arbitrage portfolios when considering the carbon and/or energy (oil, natural gas and coal) futures as investment objects. Especially, for industrials firms, this study can provide clearer information for them to decide in buying or selling allowances of CO2 emissions. The government plays a crucial role in providing related information of risk and establishing appropriate policies. When the market becomes more transparent, investors can reduce market risk to a certain extent. Last but not the least, it needs to be mentioned that the empirical results obtained from the collected data and developed models required considering the environment and the time frame in which the research take place. When using the results of this research, one ought to bear in mind that business environments are subject to frequent and constant fluctuations. Thus, changes need to be taken into account in order to gain more realistic conclusion.