存在方差持续性的资本资产定价模型分析

自回归条件异方差(ARCH) 类模型突破了传统计量经济分析的同方差假定,对现代资本 资产定价理论产生了深远的影响. 随着对时变方差研究的深入,方差持续性也日益受到人们的 重视. 文章首先介绍了条件均值、条件方差以及在自回归条件异方差的基础上介绍了方差持续 性的有关概念和性质,并将之用于资本资产定价模型的研究,讨论了条件方差持续性对资本资 产定价模型的影响,并且进一步讨论了在多资产条件下向量GARCH 模型持续性对组合投资 的影响.     

基于保守性偏差的行为资产定价模型

本文采用信息经济学、微观结构理论和动态规划的方法,从考察投资者接受新信息后形成对资产价值的后验看法入手,通过理论建模,根据投资者认知偏差的保守性偏差因素建立了一个行为资产定价模型。在基于完全理性的资产定价模型和投资者对信息的贝叶斯学习模型的基础上,文章先构建了一个基于保守性偏差的行为资产定价模型,然后用其证明了保守性偏差会导致资产误定价,最后用保守性偏差引起资产的误定价来解释了证券收益率的异象。     

参数不确定性下资产配置的动态均值-方差模型

现有关于资产配置的动态均值-方差模型的研究均假设投资者准确知道与资产收益率相关的参数,从而忽略了参数不确定性对投资决策的影响.本文研究引入参数不确定性和贝叶斯学习时的动态均值-方差模型,使用鞅方法求解得出最优投资策略的解析表达式,并导出了均值-方差有效边界.在此基础上,利用中国证券市场的实际数据进行了实证分析,结果表明参数不确定性对最优投资策略以及投资效果有较大的影响.     

变结构线性回归模型显著性检验的贝叶斯方法

以贝叶斯准则为基础, 提出了检验变结构线性回归模型显著性的一种方法. 在干扰项方差相等和不相等两种情形下, 推导出其应用的一般公式. 文中以实例说明了此方法的有效性.     

ARIMA-EGARCH-M-GED模型在公路客运量预测中的应用

首先阐述了公路客运量预测的重要性,分析回顾了基于ARMA的时间序列方法和RBF神经网络方法.针对上述方法的不足之处,为进一步提高预测结果的准确性,提出了ARIMA-EGARCH-M-GED模型.EGARCH-M模型能很好地刻画数据的异方差特性,同时还能体现正、负冲击带来的非对称效应,有很大的灵活性.再利用拉格朗日乘数法对其残差序列作检验,发现存在明显的异方差性.经验证,残差的异方差性可通过EGARCH-M-GED(自回归条件异方差)模型来拟合,建立了ARIMA-EGARCH-M-GED模型.最后提出了进一步研究的方向,以不断提高公路客运量预测的准确度.     

风险投资评价的一种新方法

本文把灰色白化权函数与资本性资产定价模型(CAPM)相结合,提出了一种新的风险投资评价方法——综合效用指数法。该方法既克服了期望值-方差法和夏普指数法的不足,又在一定程度上避免了效用函数构造的困难。     

投资组合理论选择的对比分析

投资组合理论从1952年Markowitz建立的均值-方差模型开始,先后经过了Sharpe的资产定价模型和单因素模型以及Ross等人建立的套利定价模型,而各类模型均有其相应的优势和局限。本文对以上理论模型进行了对比分析,并且总结出单因素模型应为对我国证券市场进行实证分析的首选模型。     

中国股票市场理性与非理性羊群行为实证研究

传统股票市场羊群行为检验方法的缺陷使现有的实证研究主要集中于离散状态市场羊群行为的存在性检验。根据资产定价模型因子载荷横截面离散度检验羊群行为的方法原理，结合基于产业与资本市场的资本资产定价模型，提出了股票市场理性与非理性羊群行为检验方法，并应用于中国股票市场羊群行为实证检验，直观地显现了中国股市羊群行为的动态演变特征，将股票市场羊群行为强弱转变与市场走势有机的结合起来。     

生产还是消费——中国股市生产资本资产定价模型实证检验

生产资本资产定价模型从企业利润最大化角度出发,推出资产的均衡价格.相较于消费资本资产定价模型,生产资本资产定价模型能更好的满足信息完全和决策者理性的假设条件.本文从生产资本资产定价模型出发,利用现值模型将生产的系统性风险因子,即生产的贝塔因子分解为现金流贝塔和折现率贝塔,并采用社会总投资和股市数据进行实证检验.本文发现生产-现金流贝塔是中国股市的重要定价因子,可较好地解释股权溢价的截面差异.相比之下,消费资本资产定价模型对不同资产之间风险溢价的解释能力较弱.     

基于样本外分布回测的随机过程统计推断方法

采用不同的随机过程模型描述标的资产的价格动态,会极大的影响衍生品定价和风险管理活动。在文献中,同一资产采用的随机过程往往是不一致甚至是矛盾的。本文以GBM过程与OU过程为例,提出了一种统计推断方法,旨在从多个备选模型中选出能更好的描述标的资产价格动态的随机过程。该方法应用事后检验原理,将数据分成估计窗和检验窗,估计窗用来估计随机过程的参数,然后在模型参数不变的假定下,推导了原假设成立时检验窗各个时点的资产价格的样本外分布,看实际数据落在接受域或拒绝域的频数来判断是否接受原假设。本文以大宗商品、汇率、利率、股票作为标的资产,对随机过程选择进行了实证分析。实证结果表明,一些经常使用的随机过程模型并不一定是最优的模型。     

Robust Mechanism Design

The mechanism design literature assumes too much common knowledge of the environment among the players and planner. We relax this assumption by studying mechanism design on richer type spaces. We ask when ex post implementation is equivalent to interim (or Bayesian) implementation for all possible type spaces. The equivalence holds in the case of separable environments; examples of separable environments arise (1) when the planner is implementing a social choice function (not correspondence) and (2) in a quasilinear environment with no restrictions on transfers. The equivalence fails in general, including in some quasilinear environments with budget balance. In private value environments, ex post implementation is equivalent to dominant strategies implementation. The private value versions of our results offer new insights into the relationship between dominant strategy implementation and Bayesian implementation.     

Robust Stochastic Lot‐Sizing by Means of Histograms

Traditional approaches in inventory control first estimate the demand distribution among a predefined family of distributions based on data fitting of historical demand observations, and then optimize the inventory control using the estimated distributions. These approaches often lead to fragile solutions whenever the preselected family of distributions was inadequate. In this article, we propose a minimax robust model that integrates data fitting and inventory optimization for the single‐item multi‐period periodic review stochastic lot‐sizing problem. In contrast with the standard assumption of given distributions, we assume that histograms are part of the input. The robust model generalizes the Bayesian model, and it can be interpreted as minimizing history‐dependent risk measures. We prove that the optimal inventory control policies of the robust model share the same structure as the traditional stochastic dynamic programming counterpart. In particular, we analyze the robust model based on the chi‐square goodness‐of‐fit test. If demand samples are obtained from a known distribution, the robust model converges to the stochastic model with true distribution under generous conditions. Its effectiveness is also validated by numerical experiments.     

基于Frank-Copula贝叶斯估计的系统性风险对样本标度的敏感性分析

研究表明数据样本标度的选取对系统性风险估计结果具有直接影响,本文基于150只A股样本数据构建25组投资组合,通过Copula贝叶斯估计方法获得系统风险β与投资标度比λ无信息先验的联合后验分布,对系统风险β与投资标度比λ的影响效应进行了分析。研究表明,样本标度对我国A股市场的系统性风险值估计存在误差性影响,且该影响随着公司规模的增加而不断上升,即样本标度的选取对于大额机构投资者影响较大,但数据对于账面市值比不敏感。以月度数据为基础数据,我国市场系统性风险值与样本标度比存在弱负相关关系,相比较以机构投资者占据资本市场主流的美国数据,我国市场真实投资标度存在明显差异。     

Nonparametric Predictive Inference for Exposure Assessment

Exposure assessment for food and drink consumption requires the combining of information about people's consumption of products with concentration data sets to provide predictions for chemical intake by humans. In this article, we present a method called nonparametric predictive inference (NPI) for exposure assessment. NPI is a distribution‐free method relying only on Hill's assumption . Effectively, is a postdata exchangeability assumption, which is a natural starting point for nonparametric statistics. For further discussion we refer to works by Hill and Coolen. We illustrate how NPI can be implemented to produce predictions for an individual's exposure based on consumption, body weight, and concentration data. NPI has the advantage that we do not have to assume a distribution to implement it. There may, however, be information available to suggest a distribution for a random quantity. Therefore, we present an NPI‐Bayes hybrid method where this information can be taken into account by using Bayesian methods while using NPI for the other random quantities in the model.     

A Bayes Linear Bayes Method for Estimation of Correlated Event Rates

Typically, full Bayesian estimation of correlated event rates can be computationally challenging since estimators are intractable. When estimation of event rates represents one activity within a larger modeling process, there is an incentive to develop more efficient inference than provided by a full Bayesian model. We develop a new subjective inference method for correlated event rates based on a Bayes linear Bayes model under the assumption that events are generated from a homogeneous Poisson process. To reduce the elicitation burden we introduce homogenization factors to the model and, as an alternative to a subjective prior, an empirical method using the method of moments is developed. Inference under the new method is compared against estimates obtained under a full Bayesian model, which takes a multivariate gamma prior, where the predictive and posterior distributions are derived in terms of well‐known functions. The mathematical properties of both models are presented. A simulation study shows that the Bayes linear Bayes inference method and the full Bayesian model provide equally reliable estimates. An illustrative example, motivated by a problem of estimating correlated event rates across different users in a simple supply chain, shows how ignoring the correlation leads to biased estimation of event rates.     

Feedback Mechanisms,Judgment Bias,and Trust Formation in Online Auctions*

Online markets, like eBay, Amazon, and others rely on electronic reputation or feedback systems to curtail adverse selection and moral hazard risks and promote trust among participants in the marketplace. These systems are based on the idea that providing information about a trader's past behavior (performance on previous market transactions) allows market participants to form judgments regarding the trustworthiness of potential interlocutors in the marketplace. It is often assumed, however, that traders correctly process the data presented by these systems when updating their initial beliefs. In this article, we demonstrate that this assumption does not hold. Using a controlled laboratory experiment simulating an online auction site with 127 participants acting as buyers, we find that participants interpret seller feedback information in a biased (non‐Bayesian) fashion, overemphasizing the compositional strength (i.e., the proportion of positive ratings) of the reputational information and underemphasizing the weight (predictive validity) of the evidence as represented by the total number of transactions rated. Significantly, we also find that the degree to which buyers misweigh seller feedback information is moderated by the presentation format of the feedback system as well as attitudinal and psychological attributes of the buyer. Specifically, we find that buyers process feedback data presented in an Amazon‐like format—a format that more prominently emphasizes the strength dimension of feedback information—in a more biased (less‐Bayesian) manner than identical ratings data presented using an eBay‐like format. We further find that participants with greater institution‐based trust (i.e., structural assurance) and prior online shopping experience interpreted feedback data in a more biased (less‐Bayesian) manner. The implications of these findings for both research and practice are discussed.     

Ordinally Bayesian Incentive Compatible Voting Rules

We study strategic voting after weakening the notion of strategy‐proofness to Ordinal Bayesian Incentive Compatibility (OBIC). Under OBIC, truth‐telling is required to maximize the expected utility of every voter, expected utility being computed with respect to the voter's prior beliefs and under the assumption that everybody else is also telling the truth. We show that for a special type of priors, i.e., the uniform priors, there exists a large class of social choice functions that are OBIC. However, for priors that are generic in the set of independent beliefs, a social choice function is OBIC only if it is dictatorial. This result underlines the robustness of the Gibbard–Satterthwaite Theorem.     

某大型装置现场可靠性的贝叶斯分析

可靠性、维修性和可用性是描述复杂装置质量属性的三个重要指标。在某大型装置的现场故障和维修数据的经验分析基础上,运用分段线性模型确定了装置可靠性改进趋势。针对装置现场故障和维修数据的小样本问题,给出了装置现场可靠性和维修性的贝叶斯模型,并用WinBUGS软件对其进行了求解计算。继而分析给出了装置可用性最恰当描述——任务可用度的闭合表达形式。最后,通过与极大似然法进行对比,发现应用贝叶斯方法分析该大型装置现场可靠性更有效。     

Estimation of a Benchmark Dose in the Presence or Absence of Hormesis Using Posterior Averaging

U.S. Environment Protection Agency benchmark doses for dichotomous cancer responses are often estimated using a multistage model based on a monotonic dose‐response assumption. To account for model uncertainty in the estimation process, several model averaging methods have been proposed for risk assessment. In this article, we extend the usual parameter space in the multistage model for monotonicity to allow for the possibility of a hormetic dose‐response relationship. Bayesian model averaging is used to estimate the benchmark dose and to provide posterior probabilities for monotonicity versus hormesis. Simulation studies show that the newly proposed method provides robust point and interval estimation of a benchmark dose in the presence or absence of hormesis. We also apply the method to two data sets on carcinogenic response of rats to 2,3,7,8‐tetrachlorodibenzo‐p‐dioxin.     

Insurance Policies for Monetary Policy in the Euro Area

In this paper, we aim to design a monetary policy for the euro area that is robust to the high degree of model uncertainty at the start of monetary union and allows for learning about model probabilities. To this end, we compare and ultimately combine Bayesian and worst‐case analysis using four reference models estimated with pre–European Monetary Union (EMU) synthetic data. We start by computing the cost of insurance against model uncertainty, that is, the relative performance of worst‐case or minimax policy versus Bayesian policy. While maximum insurance comes at moderate costs, we highlight three shortcomings of this worst‐case insurance policy: (i) prior beliefs that would rationalize it from a Bayesian perspective indicate that such insurance is strongly oriented towards the model with highest baseline losses; (ii) the minimax policy is not as tolerant towards small perturbations of policy parameters as the Bayesian policy; and (iii) the minimax policy offers no avenue for incorporating posterior model probabilities derived from data available since monetary union. Thus, we propose preferences for robust policy design that reflect a mixture of the Bayesian and minimax approaches. We show how the incoming EMU data may then be used to update model probabilities, and investigate the implications for policy. (JEL: E52, E58, E61)