融资融券交易与市场稳定性:基于动态视角的证据

通过构建“外生信息冲击的门限自回归条件密度(TARCD-X)”模型,进而从动态的视角考察了融资融券实施前后,市场涨跌和交易量增减4种重要的信息冲击对下一期市场稳定性的影响具有怎样的差异,以及融资融券实施后,融资融券余额变动作为新的信息冲击如何影响下一期市场的稳定性.以波动性、暴涨暴跌的不对称性和暴涨暴跌的频繁性3个指标衡量市场的稳定性,研究发现:(1)12种影响关系中,除交易量减小对市场波动性的冲击作用在融资融券实施后有所放大之外,其他11种影响关系均未出现不利变化;(2)融资融券余额的变动没有显著增加市场的波动性和暴涨暴跌的频繁性,但其与暴涨暴跌的不对称性存在显著的相关关系.后者为构建股市暴涨暴跌的预警指标提供了实证依据.     

Compulsory Income Management in the Northern Territory – evaluating its impact

Australia has been experimenting with constraining the ways in which welfare recipients can spend their income support payments, limiting their ability to access cash and purchase some products. The policy objectives include to reduce spending on alcohol, gambling, pornography and tobacco in favour of meeting ‘basic’ family needs, especially for children, to limit the scope for financial harassment, encourage pro‐social behaviours, and build financial capabilities. In the logic of the programs these outcomes are expected to be manifest at the individual, family and community levels. The policy has primarily impacted on Indigenous Australians as a result of its geographic targeting, although a recent report has recommended a more stringent version of the program be introduced universally to all welfare recipients other than the aged. The largest of these experiments is ‘New Income Management’ in the Northern Territory, which has had more than 35,000 participants since its introduction in 2010. This article reports on the key findings of the major independent evaluation of New Income Management commissioned by the Australian Government.     

Non‐parametric Estimation of Extreme Risk Measures from Conditional Heavy‐tailed Distributions

In this paper, we introduce a new risk measure, the so‐called conditional tail moment. It is defined as the moment of order a ≥ 0 of the loss distribution above the upper α‐quantile where α ∈ (0,1). Estimating the conditional tail moment permits us to estimate all risk measures based on conditional moments such as conditional tail expectation, conditional value at risk or conditional tail variance. Here, we focus on the estimation of these risk measures in case of extreme losses (where α ↓0 is no longer fixed). It is moreover assumed that the loss distribution is heavy tailed and depends on a covariate. The estimation method thus combines non‐parametric kernel methods with extreme‐value statistics. The asymptotic distribution of the estimators is established, and their finite‐sample behaviour is illustrated both on simulated data and on a real data set of daily rainfalls.     

Covariate Decomposition Methods for Longitudinal Missing‐at‐Random Data and Predictors Associated with Subject‐Specific Effects

Investigators often gather longitudinal data to assess changes in responses over time within subjects and to relate these changes to within‐subject changes in predictors. Missing data are common in such studies and predictors can be correlated with subject‐specific effects. Maximum likelihood methods for generalized linear mixed models provide consistent estimates when the data are ‘missing at random’ (MAR) but can produce inconsistent estimates in settings where the random effects are correlated with one of the predictors. On the other hand, conditional maximum likelihood methods (and closely related maximum likelihood methods that partition covariates into between‐ and within‐cluster components) provide consistent estimation when random effects are correlated with predictors but can produce inconsistent covariate effect estimates when data are MAR. Using theory, simulation studies, and fits to example data this paper shows that decomposition methods using complete covariate information produce consistent estimates. In some practical cases these methods, that ostensibly require complete covariate information, actually only involve the observed covariates. These results offer an easy‐to‐use approach to simultaneously protect against bias from both cluster‐level confounding and MAR missingness in assessments of change.     

通过课堂互动减少大学校园里的偏见和歧视

偏见和歧视是社会心理学中的两个概念,在学习过程中,采用课堂讨论这种互动方式,使同学们跳出书本内容的局限,从知识的概念、社会现实中的表现、产生的原因、如何减少偏见和歧视这四个方面去理解和探索,师生共同探讨减少偏见和歧视的方法,可达到减少大学校园里同学之间存在的偏见和歧视的效果.     

Analysing truncated data with semiparametric transformation models

Left-truncation often arises when patient information, such as time of diagnosis, is gathered retrospectively. In some cases, the distribution function, say G(x), of left-truncated variables can be parameterized as G(x; θ), where θ∈Θ?R^q and θ is a q-dimensional vector. Under semiparametric transformation models, we demonstrated that the approach of Chen et al. (Semiparametric analysis of transformation models with censored data. Biometrika. 2002;89:659–668) can be used to analyse this type of data. The asymptotic properties of the proposed estimators are derived. A simulation study is conducted to investigate the performance of the proposed estimators.     

VaR与CVaR的敏感性凸性及其核估计

风险价值（VaR）和条件风险价值（CVaR）是目前两大主流风险度量工具,如何准确地对它们进行估计是风险管理实践中首要而核心的问题。近年来非参数核估计方法因模型设定灵活、方便处理变量相依结构等优点备受关注。在本文,我们在核估计的框架内讨论VaR和CVaR估计量的性质;给出投资组合VaR和CVaR对组合头寸的一阶导数向量和二阶导数矩阵的核估计公式,并用它们来讨论组合VaR和CVaR对组合头寸的敏感性和凸性。最后,我们利用中国外汇市场的实际数据做实证分析。     

基于GARCHSK的铜期货VaR估计方法研究

基于GARCHSK模型对期货收益序列的条件偏度和峰度进行动态建模,提出了“有偏”和“尖峰厚尾”分布下的VaR估计方法。通过对沪铜期货的实证研究表明,其收益分布存在明显的“有偏”和“尖峰厚尾”。基于不同分布假定下的VaR估计结果的Kupiec检验表明,基于GARCHSK的VaR估计方法能够有效提高VaR的估计精度。     

Strategies for handling missing data in longitudinal studies with questionnaires

Missing data methods, maximum likelihood estimation (MLE) and multiple imputation (MI), for longitudinal questionnaire data were investigated via simulation. Predictive mean matching (PMM) was applied at both item and scale levels, logistic regression at item level and multivariate normal imputation at scale level. We investigated a hybrid approach which is combination of MLE and MI, i.e. scales from the imputed data are eliminated if all underlying items were originally missing. Bias and mean square error (MSE) for parameter estimates were examined. ML seemed to provide occasionally the best results in terms of bias, but hardly ever on MSE. All imputation methods at the scale level and logistic regression at item level hardly ever showed the best performance. The hybrid approach is similar or better than its original MI. The PMM-hybrid approach at item level demonstrated the best MSE for most settings and in some cases also the smallest bias.     

Efficient Robust Estimation for Linear Models with Missing Response at Random

Coefficient estimation in linear regression models with missing data is routinely carried out in the mean regression framework. However, the mean regression theory breaks down if the error variance is infinite. In addition, correct specification of the likelihood function for existing imputation approach is often challenging in practice, especially for skewed data. In this paper, we develop a novel composite quantile regression and a weighted quantile average estimation procedure for parameter estimation in linear regression models when some responses are missing at random. Instead of imputing the missing response by randomly drawing from its conditional distribution, we propose to impute both missing and observed responses by their estimated conditional quantiles given the observed data and to use the parametrically estimated propensity scores to weigh check functions that define a regression parameter. Both estimation procedures are resistant to heavy‐tailed errors or outliers in the response and can achieve nice robustness and efficiency. Moreover, we propose adaptive penalization methods to simultaneously select significant variables and estimate unknown parameters. Asymptotic properties of the proposed estimators are carefully investigated. An efficient algorithm is developed for fast implementation of the proposed methodologies. We also discuss a model selection criterion, which is based on an IC_Q ‐type statistic, to select the penalty parameters. The performance of the proposed methods is illustrated via simulated and real data sets.