Price Volatility And Risk Management Models For Carbon Market Complex System

The carbon market is cost-effective for addressing climate change, with the response to the rapid development of the Climate Change Action and the international carbon market, carbon market and its financial property has become one of the hotspot fields of energy economics research. Carbon market especially the carbon price volatility is universally acknowledged to be commonly influenced by numerous factors and appears a typical complex system, with nonlinear, nondeterministic, comprehensive and dynamic features etc. During recent years, the confluence of many contributors, such as international negotiations, the financial crises, important notices, has led to great volatility and complex changes. Carbon market risk has been brought into focus. However, existing related literature appear to be scanty to quantify the carbon market risk; as for the research methods, they tend to use some traditional paths, such as the normal distribution, linear regression and ordinary least squares (OLS) etc., which seem to be quite limited and insufficient to well define the complexity of carbon market risk management.Motivated by several scientific topics within carbon price volatility and market risk management, this thesis singles out some empirical study methodologies, including econometric models, statistical approaches and financial market risk management theories etc., so as to model the features of carbon price volatility, discuss the directions of carbon market risk information transfer, and analyze the primary factors of oil price changes and their influencing mechanism. A relatively systematic recognition for the main characteristics of oil market risk management will come into being and the carbon market risk management techniques and cost will be analyzed.In brief, the highlights of our research in this thesis can be summarized as follows.(1) By proposing the hypotheses for carbon price volatility, variance ratio and Ensemble Empirical Mode Decomposition (EEMD) will be used to analyze the carbon price. Results show that carbon market is temperature-sensitive, affected by seasonal changes, which presents a style of movement amplitude; carbon price is affected by the market mechanism at a high frequency, with the duration being less than20weeks and amplitudes less than3euros; heterogeneity environment has an impact on carbon price at a low frequency, the duration lasting more than35weeks or even more and amplitudes more than6euros or higher. Historical carbon price change shows the long-term trend declines gradually since2005from18to16euros per ton. The continuing declining trend agrees with special events by time. Analyzing the composition of carbon price from low frequencies and high frequencies helps understand the underlying rules of carbon price reality(2) Based on the research, we analyze carbon price volatility from a nonlinear dynamics point of view. First, we use a random walk model, including serial correlation and variance ratio tests, to determine whether carbon price history information is fully reflected in current carbon price. The empirical research results show that carbon price is not a random walk:the price history information is not fully reflected in current carbon price. Second, use R/S, modified R/S and ARFIMA to analyse the memory of carbon price history. For the period April2005-December2008, the modified Hurst index of the carbon price is0.4859and the d value of ARFIMA is0.1191, indicating short-term memory of the carbon price. Third, we use chaos theory to analyse the influence of the carbon market internal mechanism on carbon price, i.e., the market’s positive and negative feedback mechanism and the heterogeneous environment. Chaos theory proves that the correlation dimension of carbon price increases. The maximal Lyapunov exponent is positive and large. There is no obvious complex endogenous phenomenon of nonlinear dynamics the carbon price fluctuation. The carbon market is mildly chaotic, showing both market and fractal market characteristics. Price fluctuation is not only influenced by the internal market mechanism, but is also impacted by the heterogeneous environment.(3) Zipf analysis technology is used to assess carbon price volatility under different expectations of returns and time scales. The results show the sensitivity of the futures returns to the market’s returns is lower in the second phase than the first phase. At longer time scales, the probability of prices declining becomes greater than the probability of prices increasing. Traders with different expectations of returns have different price perceptions. For traders with low expectations of returns, carbon prices are affected by market mechanisms, seasonal weather variations and other heterogeneous events, and carbon price fluctuations are relatively well perceived. Carbon prices are more volatile and higher risks and uncertainties are more characteristic for high expectations of returns.(4) For the spot and futures prices dependencies carbon market, the chapter uses ①CC-GARCH to analyze the dynamic correlation between spot and futures price,②tructural breakpoint detection model to analyze the major events of carbon market,③copula function to analyze the dependencies change before and after the major events in the spot and futures. The results show that there is a strong correlation between spot and futures. The explanatory power of spot price volatility by the futures price is more than50%. A major special event had a visible change on correlation between spot and futures, whose influence is large. The correlation presents enhancements to the weakening trend characteristics in the first phase and the correlation showed a weakening trend after the financial crisis in the second phase, which show that the carbon market development is influenced by special events.(5) Carbon market risk directly affects the investor confidence and emission reduction results. In the present study, extreme value theory (EVT) is used to analyze risk exposure for carbon price and to measure the Value at Risk (VaR) for the carbon market. GARCH models are applied to establish a model of price volatility for the spot market and the futures market and to calculate dynamic VaR. Traditional VaR and VaR based on EVT are also compared. The results show that the downside risk is higher than the upside risk for the carbon market. Upside and downside risks are higher in the first phase (Jun2005-Dec2007) than in the second phase (Feb2008-Dec2009) for both the spot and futures markets. Upside and downside risks are similar for the spot and futures markets during the same phase. The results also show that the EVT VaR is more effective than the traditional method, which can reduce the risks for market participants. Dynamic VaR based on GARCH and EVT can effectively measure the EU ETS market risk.(6) For liquidity risk in carbon markets, the chapter builds carbon market liquidity indicators from trading spreads, trading volume, turnover rate for the first time, analyzing the liquidity risk in carbon markets. The Generalized Pareto Distribution and the Copula function is used to analyze dependencies and integration of market risk and liquidity risk market risk. The Empirical study found that the dependency of market risk and liquidity risk in carbon markets is weak. Subject to market size restrictions, the market liquidity risk in carbon market is small, but ignoring market liquidity will underestimate carbon market risk, liquidity risk reach9%of the market risk in one day for each contract, which shows liquidity risk increasing greatly for investors holding lots of contracts. Carbon market development can help to reduce liquidity risk, but liquidity risk still cannot be ignored with the turnover rate becoming smaller.(7) The behavioral characteristics of hedgers determine the hedging objective function in carbon market. Starting from a behavioral finance point of view, the chapter uses non-expected utility model of disappointment aversion to①analyze the optimal hedging strategy influence by disappointment aversion and risk aversion;②simulate hedge costs of the carbon market. The results show that the hedge ratio is small according to the minimum variance, which is about0.1in the first phase of and0.4in the second phase. The optimal hedge ratio is smaller than the general market. In carbon market, the market judgment is prone to bias when disappointment aversion coefficient and risk aversion coefficient is high or very low. When the disappointment and the risk aversion are big, the reference point of investment income will decline in the carbon market. Investors’expectation psychological is influenced by carbon price trends. Adjusting investment mentality is conducive to market operations. The substitution effect between risk aversion and disappointment aversion is not obvious, the disappointment aversion effect maybe not increase when the risk aversion decreases. Disappointment aversion and risk aversion will change investors’optimal investment strategy. The simulation shows that the return of the futures and spot carbon market has not a linear correlation. The hedging feature is not good in carbon market.To sum up, based on the research above, this thesis is aimed to strengthen therecognition of carbon market complex system in terms of price volatility and risk management, and proposesome scientific information support for carbon price forecast, macroeconomic research, investment decision-making and market regulating of related institutions.