中国沿海风暴潮灾害易损性风险区划研究

文章在界定风暴潮灾害风险的基础上,按照风暴潮灾害风险区划原则,选取风暴潮灾害易损性风险评价指标,采用层次聚类分析、熵值法及灰色关联分析方法,构建了我国沿海风暴潮灾害易损性风险区划,将我国沿海11省市划分为5个区,揭示了我国沿海承担风暴潮灾害易损性风险的地域差异性,为因地制宜地制定防灾减灾措施与规划提供了科学依据.     

风暴潮灾害损失时间路径模拟与预测

文章基于1989-2013年我国风暴潮灾害的损失数据,采用Fourier级数扩展模型拟合了其时间路径,构建了时间序列预测模型.实证结果表明:我国风暴潮灾害损失的时间路径呈周期性波动,波动周期约为10年;我国风暴潮损失的时间路径是非收敛的,但是会在每个周期中表现出与均衡值相对均匀的偏离,属于均匀波动的时间路径;预计未来5年我国风暴潮灾害损失将处于波动上扬的半周期中,国家防灾减灾部门需要高度重视,采取适当防范措施.     

风暴潮灾害综合财产险精算定价模型探析

文章从风暴潮损失的分布拟合入手，判别风暴潮损失的损失特征，分析风暴潮灾害保险建立的可行性，进而依据保险精算理论对风暴潮灾害保险进行了定价研究；针对风暴潮灾害保险的现实可操作性，为相关部门提出了对策建议。     

风暴潮灾害损失评估的主成分模型研究

我国东南沿海是世界上风暴潮频发的地区之一,文章基于近年来东南沿海风暴潮灾害损失的历史数据,介绍了主成分分析法在风暴潮灾害损失评估中的应用原理.针对风暴潮灾害损失的构成,建立了风暴潮灾害经济损失的指标体系,同时借助实例介绍了该方法在风暴潮灾害评估中的应用.最后基于主成分得分对样本风暴潮进行了聚类分析,证明了主成分分析模型在风暴潮灾害损失评估中的有效性.     

风险投资项目风险量化模型评价

在过去几十年里,已经出现二、三十种项目风险预测与评估方法。一类为传统方法,主要包括方差分析法、资本资产定价模型中β系数确定法和财务指标分析法;另一类为非传统方法,主要包括实物期权风险评估模型、Altman的Z值评分模型以及人工神经元网络 (ANN)模型。一、传统项目风险评估模型方法 1、方差分析法方差分析法是财务管理教科书中衡量风险的基本方法。这种方法将风险投资的预期收益率视为一个随机变量,而它的方差则代表不确定程度或者风险程度。     

罕见灾难、不确定性冲击和国债风险溢价——基于非线性DSGE模型

基于三阶矩近似解下非线性动态随机一般均衡(DSGE)模型的理论框架,比较分析了罕见灾难和不确定性(包括SV和GARCH)的技术冲击对中国长期国债风险溢价的影响。研究结果表明:罕见灾难影响国债风险溢价的水平值,但不影响风险溢价的波动性;SV及GARCH冲击影响风险溢价水平值和时变性;加入罕见灾难、SV及GARCH冲击的DSGE模型能更好地拟合长期国债风险溢价的非线性和时变特征。     

沪深300股指期货风险的预警研究

文章通过对沪深300股指期货IF1012合约日收益率序列进行统计分析,继而建立GARCH(1,2)-GED模型,度量了中国股指期货市场的最大损失值CVAR。研究结果显示:该模型能很好的拟合中国股指期货长期合约的特征,在股指期货市场中,每个交易日的数据均受前期波动影响,CVAR值与原始收益率序列变化基本一致,且不存在滞后效应,因此CVAR值可作为风险预警的指标为有关监管部门与投资者服务。     

金融市场风险的极值模型及实证分析

风险值(VaR)是金融市场风险的度量指标,选择好的计算方法十分重要。文章主要应用极值理论中的门限模型来研究风险值,并对2000年以来深圳综指和上证指数的日对数收益率计算风险值和期望亏损,最后对两市的风险进行了比较。     

中国城市房地产价格波动与风险值研究

文章针对近期中国城市房地产市场波段式集中涨跌的剧烈变化,使用条件异方差模型和风险值模型,以广州为例,对房地产价格及风险值进行了研究,得出了相关结论,并提出了建议.     

VaR方法在银行贷款风险评估中的应用

一、引言自从1993年国际清算银行(BIS)宣布为抵补市场风险而引入资本需求量以来,风险价值计量模型有了很大的发展。1995年BIS对它的市场风险监管计划进行了修正,允许一些银行在计算它们所面临的市场风险暴露值时可以采用自己的内部模型,不必一定使用管理当局推荐的标准模型,从而大大促进了内部 VaR模型的发展。欧洲(1997)与美国(1998)的一些大银行已经开始用它们自己的内部模型来计算贷款风险VaR暴露值,求出抵补市场风险所需的资本需求量。为此,本文拟探讨银行不流通贷款VaR暴露值的计算方法,并将贷款风险VaR暴露值同按8%的资本充…     

植入森林碳汇的碳排放权交易机制研究

严格控制CO2排放条件下,研究基于成本最小化或利润最大化目标的碳减排路径选择,超额排放的碳源企业在自主减排、碳源企业间排放权调剂和购买森林碳汇碳中和之间决策的作用机理。研究表明:通过市场化的机制设计,使减排成本较高的碳源企业承担起应有的减排责任和排放代价,减排成本较低的碳源企业和森林碳汇供给者获得应有的利益回报,从而达到森林生态效益价值补偿在市场机制中实现。这有利于约束碳源企业碳排放行为,激励森林碳汇碳中和行为,促进经济发展与生态文明的协同与耦合。     

The Effect of Non-Stationarity on Extreme Sea-Level Estimation

The sea-level is the composition of astronomical tidal and meteorological surge processes. It exhibits temporal non-stationarity due to a combination of long-term trend in the mean level, the deterministic tidal component, surge seasonality and interactions between the tide and surge. We assess the effect of these non-stationarities on the estimation of the distribution of extreme sea-levels. This is important for coastal flood assessment as the traditional method of analysis assumes that, once the trend has been removed, extreme sea-levels are from a stationary sequence. We compare the traditional approach with a recently proposed alternative that incorporates the knowledge of the tidal component and its associated interactions, by applying them to 22 UK data sites and through a simulation study. Our main finding is that if the tidal non-stationarity is ignored then a substantial underestimation of extreme sea-levels results for most sites. In contrast, if surge seasonality and the tide–surge interaction are not modelled the traditional approach produces little additional bias. The alternative method is found to perform well but requires substantially more statistical modelling and better data quality.     

基于全球价值链视角的中美制造业双边贸易分解与失衡分析

利用WIOD数据库,从全球价值链视角对中美制造业双边贸易进行了分解与对比分析。研究表明:中美制造业出口中,国内增加值比例呈现不同程度下降趋势,其参与全球价值链程度在不断提升;中国制造业出口获益能力低于美国,且处于全球价值链相对下游位置;相对美国而言,中国制造业出口中隐含大量的国外增加值,中间产品的国外增加值比例在不断上升;中美制造业参与全球价值链环节越来越多,但国内价值链构建和发展却相对薄弱;技术类别越高的中国制造业融入全球价值链的程度相对越高,但却明显低于美国;增加值贸易统计方法大幅缩小了中美贸易失衡,且技术类别越高贸易失衡缩小幅度越大。     

基于国际资本多重动机的全球系统性风险传染路径识别

本文采用最新一代的SRISK方法测度了G20国家605家金融机构的系统性风险,并设计提出了基于高维的时变参数外溢网状矩阵,识别了全球系统性风险的传染路径和传染源。研究发现,各国系统性风险呈现明显增强态势,在危机时刻具有较强同步性,尤其是新冠肺炎疫情期间各国系统性风险同步激增。而欧美等发达金融市场更容易成为全球系统性风险的风险源,其中,美国溢出指数显著高于其他国家,是全球系统性风险的主要输出方;反之,新兴经济体的金融市场成熟度与放开程度远不及欧美发达国家,因此,更多地扮演着风险吸收方角色。基于面板模型的实证结果表明,资本流动骤停强化了系统性风险跨国别传染的溢出效应和吸收效应;国际资本流动的套利和套汇动机则是影响全球系统性风险传染的两个重要渠道,特别是,债券市场暴涨所引发的套利行为对吸收效应和溢出效应的影响存在显著的非对称性,而汇率升值超调引发的套汇行为则会增强系统性风险的溢出效应和吸收效应。此外,套价动机并未对系统性风险传染产生影响,一个主要原因可能在于各国的股票市场相对债券市场较为封闭,这势必隔断了套价行为对系统性风险传染效应的影响。最后,基于全球研究结论提出中国防范外部系统性风险冲击的政策建议。     

Risk Prediction for Prostate Cancer Recurrence Through Regularized Estimation with Simultaneous Adjustment for Nonlinear Clinical Effects

In biomedical studies, it is of substantial interest to develop risk prediction scores using high-dimensional data such as gene expression data for clinical endpoints that are subject to censoring. In the presence of well-established clinical risk factors, investigators often prefer a procedure that also adjusts for these clinical variables. While accelerated failure time (AFT) models are a useful tool for the analysis of censored outcome data, it assumes that covariate effects on the logarithm of time-to-event are linear, which is often unrealistic in practice. We propose to build risk prediction scores through regularized rank estimation in partly linear AFT models, where high-dimensional data such as gene expression data are modeled linearly and important clinical variables are modeled nonlinearly using penalized regression splines. We show through simulation studies that our model has better operating characteristics compared to several existing models. In particular, we show that there is a non-negligible effect on prediction as well as feature selection when nonlinear clinical effects are misspecified as linear. This work is motivated by a recent prostate cancer study, where investigators collected gene expression data along with established prognostic clinical variables and the primary endpoint is time to prostate cancer recurrence. We analyzed the prostate cancer data and evaluated prediction performance of several models based on the extended c statistic for censored data, showing that 1) the relationship between the clinical variable, prostate specific antigen, and the prostate cancer recurrence is likely nonlinear, i.e., the time to recurrence decreases as PSA increases and it starts to level off when PSA becomes greater than 11; 2) correct specification of this nonlinear effect improves performance in prediction and feature selection; and 3) addition of gene expression data does not seem to further improve the performance of the resultant risk prediction scores.     

中国人口出生率下降与经济发展

中国人口出生率下降以及人口老龄化引起了人们对计划生育政策调整的激烈讨论。作者根据全国27个地区1980~2011年的面板数据,在横截面存在相关性的情形下,运用面板单位根和协整方法,定量考察了人口出生率、人口死亡率、城镇职工工资水平、农村居民收入水平之间的关系。研究发现：1）尽管我国实行了计划生育政策,但人口出生率与经济发展变量之间仍然存在着长期稳定的关系;2）城市和农村的经济发展对于人口出生率的影响存在显著差异。前者会刺激人口出生率的提高,但后者会降低人口出生率,且影响程度比前者大。3）人口出生率与经济发展变量之间的关系存在明显的地区差异。根据以上结论,作者认为放松计划生育政策不会引起我国人口数量猛增,反而会优化人口结构,提高人口整体素质。     

Longitudinal data analysis of mean passage time among malnutrition states: an application of Markov chains

Clinical prognosis of patients can be best described from a longitudinal study and a Markov regression model is an appropriate way of analyzing the prognosis of disease when the outcomes are serially dependent. Mean first passage time (MFPT) is a method to estimate the average number of transitions between the states of a Markov chain. The present study used the secondary data from a longitudinal study which was done during 1982–1986. This study was to illustrate the MFPT among the states of malnutrition, which were classified as Normal, Mild/Moderate and Severe among children aged 5–7 years, in South India. The 95% confidence interval (CI) for the MFPT was calculated using Monte Carlo simulation. Markov regression models were used to test for the association of state transitions across the risk factors. The average time taken for an underweight child to transit from Severe state of malnutrition to become Normal was nearly 2.73 (95% CI 2.60–2.86) years and 3.41 (95% CI 3.25–3.58) years in Rural area and 2.31(95% CI 2.20–2.42) in Urban area. The significant difference between the MFPT for some risk factors are useful to plan interventions. It will especially be useful to find the impact of duration among school-going children on their cognitive disorders.     

中国快递业发展的区域不平衡性测度研究

选取大陆31个省份2007—2014年样本数据,分别测算三区域、八区域分组下快递业发展的不平衡性及极化趋势。研究结果表明:中国快递业发展存在较强的区域不平衡性,且这种不平衡性还呈现不断加大趋势;东中西快递业发展不平衡性明显,总体呈现东部强中西部弱的特点,基尼系数分解结果显示,东中西组间差异为不平衡性贡献了70%以上的份额,同时东部和西部地区内部也存在相当程度的不平衡性;八区域不平衡性也非常明显,总体呈现沿海强内陆弱、东强西弱、南强北弱的基本格局,组间差异贡献了88%左右的份额,就区域内而言,南部沿海和西北地区不平衡性较强,东部沿海、长江中游及东北地区则较为平衡;三区域及八区域分组下快递业不平衡发展均呈现出不断增强的极化趋势。     

The cost of not knowing the radius

Robust Statistics considers the quality of statistical decisions in the presence of deviations from the ideal model, where deviations are modelled by neighborhoods of a certain size about the ideal model. We introduce a new concept of optimality (radius-minimaxity) if this size or radius is not precisely known: for this notion, we determine the increase of the maximum risk over the minimax risk in the case that the optimally robust estimator for the false neighborhood radius is used. The maximum increase of the relative risk is minimized in the case that the radius is known only to belong to some interval [r _l ,r _u ]. We pursue this minmax approach for a number of ideal models and a variety of neighborhoods. Also, the effect of increasing parameter dimension is studied for these models. The minimax increase of relative risk in case the radius is completely unknown, compared with that of the most robust procedure, is 18.1% versus 57.1% and 50.5% versus 172.1% for one-dimensional location and scale, respectively, and less than 1/3 in other typical contamination models. In most models considered so far, the radius needs to be specified only up to a factor , in order to keep the increase of relative risk below 12.5%, provided that the radius–minimax robust estimator is employed. The least favorable radii leading to the radius–minimax estimators turn out small: 5–6% contamination, at sample size 100.