通货膨胀与资产价格上涨对中国社会的影响

2007年中国社会同时受到通货膨胀与资产价格大幅上涨的压力,这不仅给中国经济带来巨大冲击,也给中国社会带来深刻影响。通货膨胀"劫贫济富"的收入再分配效应比较明显,城镇低收入家庭成为最大的受害者。尽管通货膨胀在客观上增加了农民收入,但农民从粮价上涨中得到的收益相当有限。通货膨胀使低收入群体产生焦虑感,增加了人们对未来预期的不确定性,这些都可能诱发各种社会问题。资产价格大幅上涨迅速扩大了居民财产性收入的差距,加剧了低收入群体的不公平感。资产价格的大幅上涨刺激了居民追逐各类资产的热情,助长了社会成员的投机与浮躁心态。如果任由资产价格泡沫风险发展成为系统性风险,股市动荡必将危及社会稳定。2008年中国仍将面对通货膨胀与资产价格上涨的双重考验,中央及各级地方政府必须积极审慎地应对,加强宏观调控。     

From Being Unbanked to Becoming Unbanked or Unbankable: Community Experts Describe Financial Practices of Latinos in East Los Angeles

ABSTACT

Appropriate use of formal financial institutions facilitates saving and asset building. Yet 20% of the US Latino population is unbanked. In this cross-sectional qualitative study, 34 community experts were interviewed regarding financial practices in the predominantly low-income Latino and immigrant community of East Los Angeles. Thematic analysis of these in-depth, semistructured interviews suggests that immigration status fuels fears regarding banking and ultimately the persistence of unbanked status; limited financial education prompts community members to move from being unbanked to being unbankable. Techniques employed to reverse this cycle appear helpful, but ultimately overwhelmed by the magnitude of community mistrust and misinformation.     

Externalizing an ‘Asset Model’ of Activation: Creative Institutional Work by Frontline Workers in the Norwegian Labour and Welfare Service

The past two decades have brought significant shifts in Norwegian activation policy towards a joined‐up and employability‐enhancing approach to labour market inclusion in order to promote return‐to‐work despite health problems or disabilities. Utilizing a concept from health promotion, we term this approach an ‘asset model’ of activation. The Norwegian Labour and Welfare Service (NAV) and its local offices are the main agents implementing the new policy. This article aims to investigate the strategies that the frontline workers of NAV engage in, in order to externalize an ‘asset model’ in the adjacent medical field and to the general practitioners (GPs) in particular. We analyze these strategies as forms of creative institutional work – the purposive actions undertaken to change existing presumptions and opinions among relevant actors. We argue that although the new activation policy is not theirs to develop, in order to bring about changes in practice, ‘creating’ institutional work by the frontline workers is required. Our findings show that the frontline workers develop strategies in order to externalize an asset model to the GPs, as part of operationalizing an ‘activation’ reform into practice. We identify four forms of ‘creating’ institutional work undertaken by the frontline workers: ‘defining’ – enacting legislation and regulation in relation to GPs; ‘constructing normative networks’ – creating a more collaborative relationship with the GPs; ‘educating’ – teaching the GPs about the rules and regulations, and the opportunities and assistive measures they can offer to the injured; and thereby also ‘changing normative associations’ of GPs towards the activation policy.     

An Approach to Asset Pricing Under Incomplete and Diverse Perceptions

We model a dynamic, competitive market, where in every period, risk‐neutral traders trade a one‐period bond against an infinitely lived asset, with limited short‐selling of the long‐term asset. Traders lack structural knowledge and use different “incomplete theories,” all of which give statistically correct beliefs about next period's market price of the long‐term asset. The more theories there are in the market, the higher is the equilibrium price of the long‐term asset. Investors with more complete theories do not necessarily earn higher returns than those with less complete ones, who can earn above the risk‐free rate. We provide two necessary conditions for a trader to earn above the risk‐free rate.     

Bayesian Portfolio Selection: An Empirical Analysis of the S&P 500 Index 1970–1996

In this article we present a technique for implementing large-scale optimal portfolio selection. We use high-frequency daily data to capture valuable statistical information in asset returns. We describe several statistical issues involved in quantitative approaches to portfolio selection. Our methodology applies to large-scale portfolio-selection problems in which the number of possible holdings is large relative to the estimation period provided by historical data. We illustrate our approach on an equity database that consists of stocks from the Standard and Poor's index, and we compare our portfolios to this benchmark index. Our methodology differs from the usual quadratic programming approach to portfolio selection in three ways: (1) We employ informative priors on the expected returns and variance-covariance matrices, (2) we use daily data for estimation purposes, with upper and lower holding limits for individual securities, and (3) we use a dynamic asset-allocation approach that is based on reestimating and then rebalancing the portfolio weights on a prespecified time window. The key inputs to the optimization process are the predictive distributions of expected returns and the predictive variance-covariance matrix. We describe the statistical issues involved in modeling these inputs for high-dimensional portfolio problems in which our data frequency is daily. In our application, we find that our optimal portfolio outperforms the underlying benchmark.     

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 Engle , R. F. , Kozicki , S. ( 1993 ). Testing for common features . Journal of Business and Economic Statistics 11 ( 4 ): 369 – 395 . [CSA] [CROSSREF] [Taylor & Francis Online], [Web of Science ®] , [Google Scholar]), 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.     

What's News in Business Cycles

In the context of a dynamic, stochastic, general equilibrium model, we perform classical maximum likelihood and Bayesian estimations of the contribution of anticipated shocks to business cycles in the postwar United States. Our identification approach relies on the fact that forward‐looking agents react to anticipated changes in exogenous fundamentals before such changes materialize. It further allows us to distinguish changes in fundamentals by their anticipation horizon. We find that anticipated shocks account for about half of predicted aggregate fluctuations in output, consumption, investment, and employment.     

Speculative Overpricing in Asset Markets With Information Flows

In this paper, we derive and experimentally test a theoretical model of speculation in multiperiod asset markets with public information flows. The speculation arises from the traders' heterogeneous posteriors as they make different inferences from sequences of public information. This leads to overpricing in the sense that price exceeds the most optimistic belief about the real value of the asset. We find evidence of speculative overpricing in both incomplete and complete markets, where the information flow is a gradually revealed sequence of imperfect public signals about the state of the world. We also find evidence of asymmetric price reaction to good news and bad news, another feature of equilibrium price dynamics under our model. Markets with a relaxed short‐sale constraint exhibit less overpricing.     

Semiparametric Estimation of Risk–Return Relationships

This article proposes semiparametric generalized least-squares estimation of parametric restrictions between the conditional mean and the conditional variance of excess returns given a set of parametric factors. A distinctive feature of our estimator is that it does not require a fully parametric model for the conditional mean and variance. We establish consistency and asymptotic normality of the estimates. The theory is nonstandard due to the presence of estimated factors. We provide sufficient conditions for the estimated factors not to have an impact in the asymptotic standard error of estimators. A simulation study investigates the finite sample performance of the estimates. Finally, an application to the CRSP value-weighted excess returns highlights the merits of our approach. In contrast to most previous studies using nonparametric estimates, we find a positive and significant price of risk in our semiparametric setting.     

Who Is Poorest? An Asset‐based Analysis of Multidimensional Wellbeing

Recent studies have endorsed asset‐based approaches as a strategy for attaining the Millennium Development Goals. This article discusses asset indices as a diagnostic tool for these policies, systematically breaking them down into separate dimensions of private household wealth and basic public‐goods access and discussing how shortfalls in public‐goods supply can be related to deprivations in basic human capabilities under the asset framework. It then illustrates how asset indices can be used for the targeting of public infrastructure investments and private asset‐accumulation policies, with the help of a detailed case study of deprivations in household wealth and public‐service supply in Madagascar.