破产隔离、公司治理对公共设施资产证券化融资成本的影响分析

通过构建数理模型,探析了破产隔离、公司治理对公共设施资产证券化融资成本的影响机制,并以公共设施类资产证券化的优先级证券为对象进行了实证分析。研究表明:当前环境下,公共设施类资产证券化无法实现完全的破产隔离,若可实现,则能使平均融资成本降低66个基点;原始权益人的公司治理水平能够通过影响基础资产的质量对融资成本产生影响,公司治理水平每提升1%,融资成本就会下降34个基点;公共设施资产证券化的融资成本与融资规模和信用增进程度呈负相关关系,与融资期限及无风险利率呈正相关关系。     

基于实物期权的专利资产证券化中的单一专利价值评估

专利资产证券化作为一种新型融资方式已为发达国家所采用,对于推动金融创新与科技发展起到了重要的作用。我国也开始了该项交易的试点。由于被证券化专利的特点,导致传统评估方法在对其进行评估时具有局限性。文章使用实物期权法对专利资产证券化中的单一专利的价值进行了评估,并建议使用模糊综合评价法对本模型的评估结果进行修正,以获得更为接近真实值的评估值。     

资产证券化的收益分析与优化决策

一、资产证券化的收益分析 资产证券化主要牵涉到三个利益主体;资产发起人、特设机构、投资者.他们都是追求自身利益最大化的经济实体,三者的利益既相互联系,又相互冲突.因此,要使资产证券化成为三方多赢的金融创新产品,必须满足以下条件:     

资产证券化及试行途径

一、资产证券化融资的特点 资产证券化融资是与企业通过发行股票、债券筹资等不相同的新型融资方式.传统的证券融资方式(股票、债券)是以企业自身产权为清偿基础,企业对债券的本息以及股票的权益的偿付以公司全部法定财产为界.而资产证券化中证券的发行依据不是公司的全部法定财产,而是公司资产负债表中某一部分资产,证券权益的偿还是仅以被证券化资产为限.资产证券的购买者与持有人在证券到期时可获得本金和利息的偿付,而证券偿付资金来源在于担保资产创造的现金流量.如果担保资产违约拒付,资产证券的清偿也仅限于被证券化资产的数额.其基本特征如下:     

体育资产证券化的融资方式

体育资产证券化是体育融资方式的一个重要组成内容."资产证券化(Securitization of Asset)"被称为70年代末以来国际资本市场上最重要的金融创新.它源于美国的住房抵押贷款证券(Mortgage-Backed Securities简称MBS),然后扩展到汽车、船舶贷款应收帐、基础设施收费等众多领域.现在,被证券化的基础资产在理论上已突破了金融资产的范围,扩大到其它的资产形式.     

资产证券化的经济功能和发展前景

一、资产证券化的运作程序和经济功能 资产证券化(Asset securitization,AS)是近几十年来世界金融领域最重大和发展最快的金融创新,它是在特定金融环境下而产生的一种新的融资方式.资产证券化一般涉及四类利益主体.假定:一家商业银行缺少流动资金,打算将其一批住房抵押贷款、汽车抵押贷款等资产证券化,以筹集资金.涉及的四类利益主体是:贷款出售银行,一般称为原始权益人;该商业银行的客户,即拟证券化的住房抵押贷款、汽车抵押贷款的贷款人,一般称为原债务人;特定目的机构(special purpose vehicle,SPV),是购买商业银行特定资产,并以所购资产预期产生的未来现金流量作为偿付保证而发行证券的中介机构;投资者,即购买SPV所发行证券的人或机构.     

土地资产证券化产品设计与风险防范

文章在对土地资产证券化进行界定的基础上,着重研究土地资产证券化的产品设计,创造性引入跳-扩散模型对土地资产证券化进行定价,解决产品设计中的核心问题。最后对土地资产证券化的风险控制和制度环境建设进行前瞻性探讨。     

我国资产证券化发展研究

通过资产证券化方式得以传播的美国次贷危机给全球经济带来了重创,也给我国资产证券化发展带来了反思。文章分析了次贷危机的深层次原因,提出客观看待资产证券化的观点,并通过格兰杰因果检验等方法进一步探讨出资产证券化在我国良性运行的决策建议。     

对发展住房抵押贷款证券化的思考

住房抵押贷款证券化(MBS)是银行将缺乏流动性长期住房抵押贷款所形成的信贷资产从资产负债表中剥离出来,形成贷款组合,卖给从事抵押贷款证券化业务机构的特殊目标公司(SPV),从该SPV中取得出售抵押贷款的资金,然后,由SPV以这些资产作为抵押证券,再由二级市场上的中介机构将这些证券化的抵押贷款销售给投资者的融资活动。MBS作为一种金融创新工具,将住房金融市场与证券市场相联系,它不仅可以分散商业银行的风险、满足投资者效用最大化的需求,还可以增强资本市场的融资和配置功能。可从以下几个角度进行分析。(一)风险分散机理分析风险分…     

自然资源资产价值体系构建及应用

自然资源资产的价值是自然资源资产管理中的关键问题,理论界尚未建立受到广泛认可的自然资源资产价值体系。文章首先运用经济学价值理论及生态价值理论对自然资源资产的价值进行分析;其次,结合自然资源资产的概念,对自然资源资产的价值内涵进行界定,并构造了“1-2-3-n”的价值属性树形架构,其中划分了私人物品属性价值(V_Ⅰ)和公共物品属性价值(V_Ⅱ)两大价值属性集合,以及经济价值、生态价值、社会价值三类子集,形成了自然资源资产价值体系,并将其运用于价值量核算体系构建;最后,对价值体系在自然资源资产核算与管理工作中的应用进行了总结与展望。     

Factor Stochastic Volatility in Mean Models: A GMM Approach

This paper provides a semiparametric framework for modeling multivariate conditional heteroskedasticity. We put forward latent stochastic volatility (SV) factors as capturing the commonality in the joint conditional variance matrix of asset returns. This approach is in line with common features as studied by Engle and Kozicki (1993), and it allows us to focus on identication of factors and factor loadings through first- and second-order conditional moments only. We assume that the time-varying part of risk premiums is based on constant prices of factor risks, and we consider a factor SV in mean model. Additional specification of both expectations and volatility of future volatility of factors provides conditional moment restrictions, through which the parameters of the model are all identied. These conditional moment restrictions pave the way for instrumental variables estimation and GMM inference.     

Factor Multivariate Stochastic Volatility via Wishart Processes

This paper proposes a high dimensional factor multivariate stochastic volatility (MSV) model in which factor covariance matrices are driven by Wishart random processes. The framework allows for unrestricted specification of intertemporal sensitivities, which can capture the persistence in volatilities, kurtosis in returns, and correlation breakdowns and contagion effects in volatilities. The factor structure allows addressing high dimensional setups used in portfolio analysis and risk management, as well as modeling conditional means and conditional variances within the model framework. Owing to the complexity of the model, we perform inference using Markov chain Monte Carlo simulation from the posterior distribution. A simulation study is carried out to demonstrate the efficiency of the estimation algorithm. We illustrate our model on a data set that includes 88 individual equity returns and the two Fama–French size and value factors. With this application, we demonstrate the ability of the model to address high dimensional applications suitable for asset allocation, risk management, and asset pricing.     

Factor Multivariate Stochastic Volatility via Wishart Processes

This paper proposes a high dimensional factor multivariate stochastic volatility (MSV) model in which factor covariance matrices are driven by Wishart random processes. The framework allows for unrestricted specification of intertemporal sensitivities, which can capture the persistence in volatilities, kurtosis in returns, and correlation breakdowns and contagion effects in volatilities. The factor structure allows addressing high dimensional setups used in portfolio analysis and risk management, as well as modeling conditional means and conditional variances within the model framework. Owing to the complexity of the model, we perform inference using Markov chain Monte Carlo simulation from the posterior distribution. A simulation study is carried out to demonstrate the efficiency of the estimation algorithm. We illustrate our model on a data set that includes 88 individual equity returns and the two Fama-French size and value factors. With this application, we demonstrate the ability of the model to address high dimensional applications suitable for asset allocation, risk management, and asset pricing.     

Dynamic component detection in a multifactor model for stock returns

Summary In this paper the factorial structure of some asset returns quoted at the Milan stock exchange is analyzed in order to detect the presence of a dynamic component. The maximum likelihood estimates of the dynamic model, for which a space-state representation and the Kalman filter were used, are compared with the estimates of the static model via the information criteria. There is no evidence of dynamic factors underlying the analyzed samples of asset returns, while one static factor seems to be relevant. Invited paper at the Conference held in Bologna, Italy, 27–28 May 1993, on ?Statistical Tests: Methodology and Econometric Applications?.     

基于因子分析法的中国民用汽车保有量影响因素分析及其政策含义

随着中国国民收入的稳步提高,民用汽车保有量在快速上升,但理论界对民用汽车保有量的研究既存在选取变量有限、信息丢失或遗漏等问题,又存在对其影响因素研究不足的现状。故选取2005年全国31个省市自治区的各地区国民经济核算、固定资产投资、运输和邮电、国内贸易以及人口五大类中部分项目的大量截面数据,在对影响中国民用汽车保有量影响因素进行相关分析的基础上,运用因子分析法率先得出影响中国民用汽车保有量的生产消费、交通水利投资、公路和批发零售四个相关因子,并讨论了促进中国汽车发展的政策含义。     

Which Factors are Risk Factors in Asset Pricing? A Model Scan Framework

Abstract

A key question for understanding the cross-section of expected returns of equities is the following: which factors, from a given collection of factors, are risk factors, equivalently, which factors are in the stochastic discount factor (SDF)? Though the SDF is unobserved, assumptions about which factors (from the available set of factors) are in the SDF restricts the joint distribution of factors in specific ways, as a consequence of the economic theory of asset pricing. A different starting collection of factors that go into the SDF leads to a different set of restrictions on the joint distribution of factors. The conditional distribution of equity returns has the same restricted form, regardless of what is assumed about the factors in the SDF, as long as the factors are traded, and hence the distribution of asset returns is irrelevant for isolating the risk-factors. The restricted factors models are distinct (nonnested) and do not arise by omitting or including a variable from a full model, thus precluding analysis by standard statistical variable selection methods, such as those based on the lasso and its variants. Instead, we develop what we call a Bayesian model scan strategy in which each factor is allowed to enter or not enter the SDF and the resulting restricted models (of which there are 114,674 in our empirical study) are simultaneously confronted with the data. We use a Student-t distribution for the factors, and model-specific independent Student-t distribution for the location parameters, a training sample to fix prior locations, and a creative way to arrive at the joint distribution of several other model-specific parameters from a single prior distribution. This allows our method to be essentially a scaleable and tuned-black-box method that can be applied across our large model space with little to no user-intervention. The model marginal likelihoods, and implied posterior model probabilities, are compared with the prior probability of 1/114,674 of each model to find the best-supported model, and thus the factors most likely to be in the SDF. We provide detailed simulation evidence about the high finite-sample accuracy of the method. Our empirical study with 13 leading factors reveals that the highest marginal likelihood model is a Student-t distributed factor model with 5 degrees of freedom and 8 risk factors.     

地下资产存量价值变化的因素分解

The researchers often discussed that how to get the value of the stock level of sub-soil asset by net present value(NPV) when they considered the value of depletion of natural recourse in the past. They did not pay more attention to elements which impact the value of the stock level of sub-soil asset. This article introduces a general formula, then focuses on the impact of each element changes on the value of stock level of sub-soil asset in terms of quantity, it gives an illustrative example at the end.     

中国老年前期女性红细胞比积正常参考值与地理因素的关系

为制定中国老年前期女性红细胞比积正常参考值的统一标准提供科学依据,收集了中国229个地区用温氏法测定的17562例健康老年前期女性红细胞比积正常参考值,并对其与地理因素的关系进行了研究,结果发现海拔高度是影响老年前期女性红细胞比积正常参考值最主要的因素,随着海拔高度的逐渐增大,老年前期女性红细胞比积正常参考值也在逐渐的增大,相关性很显著。为此用因子分析的方法推导出了一个回归方程,如果知道了中国某地的地理因素,就可以用回归方程估算这个地区的老年前期女性红细胞比积正常参考值。依据老年前期女性红细胞比积正常参考值与地理因素的依赖关系把中国分为青藏区、西南区、西北区、东南区、华北区、东北区。     

Factor recovery by principal axis factoring and maximum likelihood factor analysis as a function of factor pattern and sample size

Principal axis factoring (PAF) and maximum likelihood factor analysis (MLFA) are two of the most popular estimation methods in exploratory factor analysis. It is known that PAF is better able to recover weak factors and that the maximum likelihood estimator is asymptotically efficient. However, there is almost no evidence regarding which method should be preferred for different types of factor patterns and sample sizes. Simulations were conducted to investigate factor recovery by PAF and MLFA for distortions of ideal simple structure and sample sizes between 25 and 5000. Results showed that PAF is preferred for population solutions with few indicators per factor and for overextraction. MLFA outperformed PAF in cases of unequal loadings within factors and for underextraction. It was further shown that PAF and MLFA do not always converge with increasing sample size. The simulation findings were confirmed by an empirical study as well as by a classic plasmode, Thurstone's box problem. The present results are of practical value for factor analysts.