管理工程学报
管理工程學報
관리공정학보
Journal of Industrial Engineering and Engineering Management
2014年
2期
39~44
,共null页
王苏生 常凯 黄杰敏 彭珂
王囌生 常凱 黃傑敏 彭珂
왕소생 상개 황걸민 팽가
碳排放 持有成本 期限结构 仿射模型 卡尔曼滤波
碳排放 持有成本 期限結構 倣射模型 卡爾曼濾波
탄배방 지유성본 기한결구 방사모형 잡이만려파
carbon emissions ; cost-of-carry ; term structure ; affine model ; Kalman filter
在延伸Cortazar和王苏生的N因素仿射模型基础上,假设多元不可观察的状态变量服从均值回复过程,我们构建一个新的碳排放持有成本N因素仿射模型.基于ICE和BLUENEXT交易所期货和现货的价差作为碳排放持有成本,作者运用卡尔曼滤波和最大似然法对碳排放持有成本仿射模型进行模拟分析.实证结果显示,碳排放持有成本表现出明显的均值回复过程,除了市场风险溢价,各状态变量的均值回复速度、方差和协方差均在5%显著水平下表现出较高的显著性.通过平均绝对误差(MAE)和均方根误差(RMSE)方法对三因素碳排放持有成本仿射模型进行拟合能力评价,模型拟合误差值均显著性低于1%,这充分说明碳排放持有成本仿射模型具有较好的拟合能力,三因素仿射模型能够较准确地模拟和预测碳排放持有成本.
在延伸Cortazar和王囌生的N因素倣射模型基礎上,假設多元不可觀察的狀態變量服從均值迴複過程,我們構建一箇新的碳排放持有成本N因素倣射模型.基于ICE和BLUENEXT交易所期貨和現貨的價差作為碳排放持有成本,作者運用卡爾曼濾波和最大似然法對碳排放持有成本倣射模型進行模擬分析.實證結果顯示,碳排放持有成本錶現齣明顯的均值迴複過程,除瞭市場風險溢價,各狀態變量的均值迴複速度、方差和協方差均在5%顯著水平下錶現齣較高的顯著性.通過平均絕對誤差(MAE)和均方根誤差(RMSE)方法對三因素碳排放持有成本倣射模型進行擬閤能力評價,模型擬閤誤差值均顯著性低于1%,這充分說明碳排放持有成本倣射模型具有較好的擬閤能力,三因素倣射模型能夠較準確地模擬和預測碳排放持有成本.
재연신Cortazar화왕소생적N인소방사모형기출상,가설다원불가관찰적상태변량복종균치회복과정,아문구건일개신적탄배방지유성본N인소방사모형.기우ICE화BLUENEXT교역소기화화현화적개차작위탄배방지유성본,작자운용잡이만려파화최대사연법대탄배방지유성본방사모형진행모의분석.실증결과현시,탄배방지유성본표현출명현적균치회복과정,제료시장풍험일개,각상태변량적균치회복속도、방차화협방차균재5%현저수평하표현출교고적현저성.통과평균절대오차(MAE)화균방근오차(RMSE)방법대삼인소탄배방지유성본방사모형진행의합능력평개,모형의합오차치균현저성저우1%,저충분설명탄배방지유성본방사모형구유교호적의합능력,삼인소방사모형능구교준학지모의화예측탄배방지유성본.
In recent years,issues related to CO2 gas emissions have attracted public attention.CO2 gas emission controlling and environment protection have become hot political and academic topics.Since the launch of European Union emissions trading scheme (EU ETS) in 2005,CO2 emissions allowance has become valuable commodity which can be transferred and exchanged in the CO2 emissions allowances market.China is now actively exploring and developing emission trading scheme in the future.Chinese market participants,such as enterprises,financial institutes,hedgers,and investors,are lack of the capability of hedging and risk management in the asset portfolio for carbon emissions.Carbon emissions market is an emerging financial market.Spot price and futures price for carbon emission has strong time-varying property.On the basis of historical trading data of cost-of-carry for carbon emission,examining mean-reversion process and accurately grasping the term structure of cost-of-carry are not only an important way for market participants to achieve market arbitrage in the spot and futures markets,but also an important management tool for market participants to optimize the assets portfolio and strengthen risk management.Government regulation policy,energy utilization efficiency,and promotion decision in low-carbon technology have driven long-run quantity of demand and supply in the carbon emissions market,and directly promoted long-term price trend for both spot and futures carbon emissions.The change of interest rate,extreme climate and energy price volatility have caused the short-run shock of expected total quantity of demand and supply in the carbon emissions market,and directly affected short-term price fluctuation for carbon emission.Based on the assumption that multivariate unobservable state variables follow mean-reverting process,we propose a new Nfactor affine model of cost-of-carry for carbon emissions to extend N-factor affine model proposed by the Cortazar and Wang.The parameters coefficients of affinity model are estimated by using cost-of-carry with different maturities extended from the prices spread between spot and futures for carbon emissions.Measurement and transition equations are used as observable variables.We provide empirical evidence of three-factor affine model of cost-of carry for carbon emissions by using Kalman filter and likelihood methods.Our empirical results show that cost-of-carry for carbon emissions has mean-reversion process.Mean-reverting speeds,volatility and covariance among the state variables are highly significant at the confidence level of 5%,while market risk premium is not significant to affect cost-of-carry for carbon emissions.Affine model robustness of cost-of-carry for carbon emissions is evaluated by using mean absolute errors (MAE) and root mean square errors (RMSE).The fitting errors of affine model are significantly less than 1%,and these signs fully show better robustness of three-factor affine model of cost-of-carry for carbon emissions.