基于Copula的QDII与排放权资产的投资组合构建

本文介绍了欧盟温室气体排放权交易市场,选择欧洲气候交易所(ECX)推出的欧盟配额(EUA)期货合约作为温室气体排放权资产(即碳资产)的代表,利用Copula函数得到了国内一支QDII基金-南方全球精选基金与该资产收益率的联合分布,并进一步据此得到了两种资产任一组合的收益率的分布函数,然后在不同的显著性水平下确定了具有最小VaR的最优组合系数。对最优组合的分析发现,本文构建的投资组合在收益率高于原QDII基金收益率的同时,其VaR值在各种显著性水平下均低于原QDII基金的VaR,并且最优的组合系数对于特定的VaR水平的敏感度不高,组合策略具有可操作性。     

基于非参数分布的沪深股市VaR测度

沪深大盘指数的收益率分布函数并不服从通常人们所认为的正态分布.因此,采用一种新的方法—非参数核密度估计,对沪深股指收益率分布进行拟合.该方法不仅很好地刻画了收益率分布的尖峰和肥尾特征,而且由此建立的VaR模型比一般的基于参数分布的VaR模型更能捕捉市场的风险特征,结论也更加准确.     

基于不同分布EGARCH模型的EU ETS价格波动和风险研究

分别基于正态分布、t分布、GED分布假设下的EGARCH模型,考察EUA和CER期货价格收益率的波动特征,并估算期货市场的风险VaR值,利用LR统计量检验VaR,估计值的准确程度.实证结果表明:碳期货收益率存在明显的"尖峰厚尾"特性;碳期货市场存在负的"杠杆效应","利多"的影响小于"利空"的影响;EUA期货市场相比CER期货市场具有更高的风险;EGARCH-GED模型对碳期货市场的风险刻画能力最强,其次是EGARCH-N模型,EGARCH-t模型刻画能力最差.     

基于二维t分布的条件VaR研究

根据多元t分布的定义及性质,推导出二维t分布随机变量差的条件分布仍服从t分布.在假定股票价格对数与收益率服从二维t分布的基础上,利用该性质,可以得到不同股票价格水平条件下,收益率的一维条件t分布,进而计算出价格条件VaR.利用多元t分布研究价格条件的收益率分布问题,与正态分布相比,较好地刻画了证券收益率分布的尖峰厚尾现象.     

基于相空间重构和最小二乘支持向量回归模型参数同步优化的碳市场价格预测

为提高碳市场价格预测的准确性,提出了一种基于相空间重构(PSR)和最小二乘支持向量回归(LSSVR)模型参数同步优化的碳市场价格预测模型(PSO-PSR-LSSVR).该模型基于碳市场价格数据特征,利用PSO算法自适应同步优化PSR和LSSVR参数,有效克服了模型参数单独优化和轮流优化的缺陷,保证了参数组合的整体最优.以欧盟碳排放交易体系(EU ETS)下两个碳期货价格为研究对象,实证结果表明,相比常用的预测方法,该模型能够获得更高的预测精度.     

中国股票市场收益率分布曲线的实证

股票价格行为的随机理论认为市场收益服从正态分布 ,但在现实中这一假设不一定成立 ,市场收益率更多地呈现出偏离正态分布的形式。本文检验中国市场的收益率分布形态。     

摩擦市场条件下的双目标投资组合模型模糊优化

首先建立了摩擦市场条件下基于收益率分布偏度水平的双目标投资组合模型.在此基础上,将模糊集合的概念引入到该模型中,用模糊数学中的线性隶属函数处理了其中的风险目标和收益目标,建立了摩擦市场条件下基于收益率分布偏度水平的模糊型双目标投资组合模型.然后,针对该模型进行了新型遗传算法设计(动态遗传算法).最后用一个具体的算例给出了该模型的一个实例最优解,体现了多样化投资分散风险的组合投资原理.     

基于经验分布的投资选择

假定股票市场是一列独立同分布的随机市场收益率的理想模型,考虑经济人在任意时刻进入市场开始投资,经过一段时间后离开市场.利用经验对数最优投资组合得到了资金的渐近最优增长率.这一结果确立了普通投资选择与滑动投资模型的密切联系.     

基于二维正态分布的条件VaR研究

基于条件收益率的VaR测算方法.在假定股票价格对数与收益率服从二维正态分布的基础上,对每一价格水平,得到条件收益率的分布特征,进而计算条件VaR值.通过分析证明了条件收益率分布与价格水平高低有关,一般价格升高会使条件收益率分布向左侧移动,反之向右侧平移.     

非参数方法在我国证券市场收益波动研究中的应用

以上海证券交易所综合指数日收益率数据为样本,讨论了非参数方法在建立股票价格模型中的应用.在此基础上,对股市收益分布进行了研究,并据此对市场走向进行了预测.     

A multi-period stochastic portfolio optimization model applied for an airline company in the EU ETS

This paper aims to set up and solve a multi-period stochastic portfolio optimization model from an airline company’s point of view, considering all the specific European Union Emissions Trading Scheme (EU ETS) regulatory, managerial and trading constraints (i.e. physical constraints). Our contribution to existing academic literature is multiple. As the first ever case, we apply this technique to the aviation sector, a newly included sector within the EU ETS. More than mainly incorporating physical and technical (‘engineering’) features and focusing on short-term planning issues, we particularly address financial features and focus on mid-term planning issues. Therefore, instead of using spot prices, we run Monte Carlo simulations of correlated geometric Brownian motions (GBM) for traded futures prices of various emission allowance types for different CO₂ delivery time periods. We thereby specifically refer to the existing exchange-traded emission allowance types EU Emission Allowance (EUA) and Certified Emission Reduction (CER). By implementing actually valid and real-world-oriented regulatory constraints for EU ETS, namely managerial and trading constraints, our model implies a real-life application. We also highlight the possibility of banking and borrowing of emission allowances between CO₂ compliance periods, which is a crucial regulatory feature of EU ETS.     

Does EU ETS instigate Air Cargo network reconfiguration? A model-based analysis

From 2012 on aviation is included in the European Emissions Trading Scheme (EU ETS) and operators have to hold one allowance per tonne of CO₂ emitted on every flight departing from and/or arriving at an airport within the EU. Now two questions are of interest: Is it profitable for airlines to reconfigure their routes to reduce EU-related emissions and costs, and, will the scheme be successful in the sense that emissions are reduced significantly. Here the potential for and the consequences of reconfiguration are different for the passenger and cargo business, respectively. In this paper we present a model-based evaluation of network (re-)configuration/optimization at cargo airlines under different EU ETS scenarios and we discuss the results with respect to the two issues raised above.     

Price dynamics in the European Union Emissions Trading System and evaluation of its ability to boost emission-related investment decisions

The price of permits in the European Union Emissions Trading System (EU ETS) has historically been highly sensitive and prone to jumps. We consider different stochastic processes to model the price of permits, and show that the Variance Gamma (VG) model provides the best fit for the price distribution, among a selection of infinite activity processes. Using this result as a starting point, we assess the effects of the EU ETS in delivering low-carbon investments at the firm level, by modeling a price taker electricity producer subject to the EU ETS jurisdiction. We compute, via Least Squares Monte Carlo, the value of the real option the greenhouse gas emitter has, consisting in the opportunity to switch from its current high-carbon technology to a cleaner one. We use a VG specification for carbon prices, and a mean-reverting (Brennan–Schwartz) process for the price of fuel. Moreover, we further analyze the investment decision problem, in case of a CO₂ price stabilization mechanism in the form of a price floor, by explicitly computing the expected value of the investment project by means of Fourier methods. Our results show that the introduction of the price stabilization mechanism significantly affects the timing of the investment decision, and supports emission-related investments.     

中国股票收益率的稳定分布拟合与检验

本文对上证综指及深证成指的收益率进行了稳定分布拟合,并与正态分布的拟合加以比较分析,结果表明稳定分布能更好的处理股票市场中的“尖峰厚尾”现象。     

Modeling fat tails in stock returns: a multivariate stable-GARCH approach

In this paper a new multivariate volatility model is proposed. It combines the appealing properties of the stable Paretian distribution to model the heavy tails with the GARCH model to capture the volatility clustering. Returns on assets are assumed to follow a sub-Gaussian distribution, which is a particular multivariate stable distribution. In this way the characteristic function of the fitted returns has a tractable expression and the density function can be recovered by numerical methods. A multivariate GARCH structure is then adopted to model the covariance matrix of the Gaussian vectors underlying the sub-Gaussian system. The model is applied to a bivariate series of daily U.S. stock returns. Value-at-risk for long and short positions is computed and compared with the one obtained using the multivariate normal and the multivariate Student’s t distribution. Finally, exploiting the recent developments in the vast dimensional time-varying covariances modeling, possible feasible extensions of our model to higher dimensions are suggested and an illustrative example using the Dow Jones index components is presented.     

CO2 emissions trading planning in combined heat and power production via multi-period stochastic optimization

The EU emissions trading scheme (ETS) taking effect in 2005 covers CO₂ emissions from specific large-scale industrial activities and combustion installations. A large number of existing and potential future combined heat and power (CHP) installations are subject to ETS and targeted for emissions reduction. CHP production is an important technology for efficient and clean provision of energy because of its superior carbon efficiency. The proper planning of emissions trading can help its potential into full play, making it become a true “winning technology” under ETS. Fuel mix or fuel switch will be the reasonable choices for fossil fuel based CHP producers to achieve their emissions targets at the lowest possible cost. In this paper we formulate CO₂ emissions trading planning of a CHP producer as a multi-period stochastic optimization problem and propose a stochastic simulation and coordination approach for considering the risk attitude of the producer, penalty for excessive emissions, and the confidence interval for emission estimates. In test runs with a realistic CHP production model, the proposed solution approach demonstrates good trading efficiency in terms of profit-to-turnover ratio. Considering the confidence interval for emission estimates can help the producer to reduce the transaction costs in emissions trading. Comparisons between fuel switch and fuel mix strategies show that fuel mix can provide good tradeoff between profit-making and emissions reduction.     

Measuring financial risk and portfolio optimization with a non-Gaussian multivariate model

In this paper, we propose a multivariate market model with returns assumed to follow a multivariate normal tempered stable distribution. This distribution, defined by a mixture of the multivariate normal distribution and the tempered stable subordinator, is consistent with two stylized facts that have been observed for asset distributions: fat-tails and an asymmetric dependence structure. Assuming infinitely divisible distributions, we derive closed-form solutions for two important measures used by portfolio managers in portfolio construction: the marginal VaR and the marginal AVaR. We illustrate the proposed model using stocks comprising the Dow Jones Industrial Average, first statistically validating the model based on goodness-of-fit tests and then demonstrating how the marginal VaR and marginal AVaR can be used for portfolio optimization using the model. Based on the empirical evidence presented in this paper, our framework offers more realistic portfolio risk measures and a more tractable method for portfolio optimization.     

中国对外直接投资的贸易效应研究:基于对北美自贸区、欧盟、东盟投资的比较分析

中国对外贸易发展成熟、国际地位显著,但从长期看,其边际收益呈现递减趋势.而对外直接投资虽规模较小,且尚在起步阶段,但其发展速度快、市场潜力巨大.在拓展Anderson和Wincoop模型基础上,理论分析了对外直接投资与贸易之间的关系.然后通过建立计量模型,利用1995-2011年面板数据进行固定模型分析.由于中国对外直接投资的贸易效应存在明显的区域性,以北美自贸区、欧盟和东盟作为研究对象,实证检验了中国对外直接投资与贸易的关系,旨在考察中国对三种不同类型区域一体化组织投资的贸易效应差异.研究结果表明,从长期看,中国对北美自由贸易区、欧盟和东盟投资均具有明显的贸易创造效应.这种贸易创造效应的大小依次是东盟欧盟北美自贸区.     

Asset pricing and portfolio selection based on the multivariate extended skew-Student-<Emphasis Type="Italic">t</Emphasis> distribution

The returns on most financial assets exhibit kurtosis and many also have probability distributions that possess skewness as well. In this paper a general multivariate model for the probability distribution of assets returns, which incorporates both kurtosis and skewness, is described. It is based on the multivariate extended skew-Student-t distribution. Salient features of the distribution are described and these are applied to the task of asset pricing. The paper shows that the market model is non-linear in general and that the sensitivity of asset returns to return on the market portfolio is not the same as the conventional beta, although this measure does arise in special cases. It is shown that the variance of asset returns is time varying and depends on the squared deviation of market portfolio return from its location parameter. The first order conditions for portfolio selection are described. Expected utility maximisers will select portfolios from an efficient surface, which is an analogue of the familiar mean-variance frontier, and which may be implemented using quadratic programming.