关于我国地震灾害损失分布函数的研究

文章以1978～2006年间我国发生的183起地震灾害事故为样本,选取地震损失额、每年发生地震次数为指标,建立我国地震灾害损失分布函数。针对地震损失额利用经验剩余函数值分析损失分布集中程度、初步估计损失分布函数,分组处理样本数据、拟合分布图像、参数估计、单个样本非参数检验,确定损失分布函数为对数正态分布;针对每年发生地震灾害次数初步估计分布函数,通过历史频率与理论概率比较判断拟合效果、确定分布函数为泊松分布。     

我国个人住房抵押贷款违约风险影响因素的实证研究

 次贷危机的爆发提醒人们重新认识个人住房抵押贷款违约带来的风险。现阶段,我国商业银行已经发放了大量个人住房抵押贷款,那么,哪些因素可能对我国个人住房抵押贷款违约产生显著影响,这是需要我们深入研究的问题。本文在实地调研的基础上,利用637组数据,对影响我国个人住房抵押贷款违约风险的因素进行了实证分析。研究结果发现,借款人受教育程度越高,工作行业稳定程度和垄断程度越高,违约率越低;借款人购买住房面积越大,每月偿还贷款金额与家庭收入之比越高,违约率越高;本地人比外地人违约率高;购买现房借款人比购买期房借款人的违约率高。对违约影响较大的因素依次是住房面积、月还款额占家庭收入比、是否期房、受教育程度。     

农作物旱灾保险费率的动态估计及预测

文章基于干旱指标SPEI指数对农作物干旱单产和趋势单产进行拟合,以此为基础结合曲靖市实例对农作物旱灾保险费率进行动态估计,并采用灰色预测模型对未来几年内农作物保险费率的数值进行预测.结果表明:农作物保险费率的数值在区间[3％,4％]呈现“W”型变化趋势,且与农作物灾损率的变化情况较为相似;农作物保险费率的变化在很大程度上受到灾害发生概率的影响,灾害发生概率大,农作物保险费率的数值偏高,而灾害发生概率小,农作保险费率的数值偏低;未来农作物保险费率的增长幅度不大,增长较为平缓.     

再保险业务风险分布特征的实证分析

再保险人的整体风险管理能力、水平和行为将直接影响再保险公司的整体风险管理绩效以及整个保险市场的稳定。本文使用极值理论模拟了再保险业务的风险分布特征,比较了成数再保险和非比例再保险业务风险分布的差异,认为再保险业务损失分布不服从正态分布,具有厚尾性;成数业务损失分布具有均值大、方差小的特点,而非比例业务损失分布的均值较小,但方差较大;在高置信水平条件下,非比例业务的风险损失率远远大于成数业务。因此,再保险公司应当大力发展非比例再保险业务,并增强资本实力,积极拓展业范围,在国际市场上分散风险。     

基于因子模型的快速灾情统计指标体系研究

 鉴于我国尚无完整的灾害损失统计制度和地震快速灾情统计指标体系，四川汶川8.0级地震对我国灾害损失统计制度方法提出了新的挑战。本文从完善统计制度和方法的角度出发，基于因子分析模型，通过模型的不断改进，试图对众多灾损指标进行多步降维处理，在尽量保持信息量丢失最少的前提下，反复比较多个模型间的有效性和一致性，筛选出稳定的、有效的重要灾损指标，构建了中国地震快速灾情统计指标体系。并以四川省138个受灾县(市、区)的数据对所建地震灾情快速反应统计指标体系进行了实证分析。结果表明：所建中国地震灾情快速反应指标体系具有简便易行、代表性强、时效性高的优势特征，与中国地震局公布的地震烈度图保持了一致性。     

湖北省水稻生产风险区划的实证研究

文章根据农作物生产风险程度大致类似的原则.运用主导指标法,以开展水稻区域产量保险为背景.选取水稻单产变异系数,水稻单产减产超过10%和20%的概率等5个主导指标,运用聚类分析法对湖北水稻生产县市进行了风险等级划分.分析结果表明,湖北水稻生产风险表现了高度的区域差异性.     

基于因子分析的地震灾害评价指标体系研究

对地震灾害造成的损失和破坏程度的评价和测度是进行地震应急和灾后重建的重要依据,如何快速评价地震灾害的程度成为决策的关键所在.文章利用因子分析的方法,对地震灾情统计的相关指标进行分析,从而构建了以经济损失程度、人员损失程度和地震震级强度为主因子的地震灾情评价的指标体系.     

中国式财政分权的数量测度

中国式财政分权并不是严格的法律意义上的分权,而是一种“事实性分权”。选择单一维度的分权指标,无法准确衡量中国式财政分权的程度。本文以1997－2007年中国的省级数据为样本,选取财政收入自治率、财政收入占比、财政支出自决率、财政支出占比、税收管理分权度、行政管理分权度6个指标,对中国式财政分权进行全景式评估;进而,运用基于Bootstrap的Shannon-Spearman测度方法,选择信息损失最小的组合指标作为中国式财政分权的有效衡量指标;最后,应用财政分权衡量指标,检验财政分权与中国经济增长的关系。实证结果显示：中国省级财政分权程度在样本期间呈逐步降低的趋势;中国式财政分权整体上不利于地方经济增长,但不同维度的分权指标对经济增长的影响并不相同。     

规模的门限效应、综合化经营与我国商业银行系统性风险

综合化经营已经成为我国商业银行应对传统业务收入比重不断降低、谋求突破性发展的重要战略。现有研究常采用非利息收入占比作为综合化经营程度的测量指标,这种做法具有明显的技术缺陷。本文将非金融企业产品多样化熵的测量指标引入到商业银行综合化经营分析,较好地解决了综合化经营程度的测量问题。基于宏观审慎与微观审慎相结合的监管理念,本文在利用成分期望损失方法测量商业银行层面系统性风险基础上,借助于动态面板门限模型重点探讨并识别了商业银行规模的门限效应,并借此区分不同的机制状态来分析综合化经营对商业银行系统性风险的影响作用。研究结果表明：综合化经营对于我国商业银行系统性风险的影响作用具有规模的门限效应,表现为规模小的商业银行开展综合化经营会降低系统性风险,规模大的商业银行开展综合化经营会增加系统性风险。     

中美石油安全水平对经济增长影响的对比研究——基于灰色关联分析

为科学地评价比较中美石油安全水平对经济增长的影响程度,文章采用主成分分析方法,选取采储比水平、储量接替率、消费弹性系数、原油价格波动系数、对外依存度和石油占一次能源消费比重等6个要素指标,构成一个新的石油安全指标评价体系,并利用灰色关联方法比较分析了不同时间段内中美两国的石油安全水平与经济增长之间的关联度,得出自21世纪以来,美国经济发展对石油安全水平的依赖程度要远大于中国的结论。     

居民家庭金融资产风险与宏观经济波动的协动性关系研究——基于GARCH-M模型的风险度量方法

居民家庭金融资产组合不仅存在风险,而且风险会随着居民持有不同金融资产的比重、家庭外部宏观经济的波动而变动。通过构建GARCH-M模型,引入宏观经济变量(GDP增长率、CPI、利率),并计算在险价值VaR,分析宏观经济指标波动与家庭金融风险的协动性关系。研究认为,家庭金融收益主要受利率影响,家庭金融风险主要受GDP和CPI影响,而且家庭金融风险具有集聚性,并会影响利率的变化。     

煤矿瓦斯爆炸事故单危险源风险度量模型研究

本文研究了煤矿瓦斯爆炸事故危险源的分类,把危险源划分为人的不安全行为、机器设备不安全状态、环境不安全特征和管理缺陷四大类,并根据煤矿现场调查各类危险源的数据以及相应数学方法的特点,提出煤矿瓦斯爆炸事故人、机器设备(物)、环境、管理四类危险源的风险度量方法,在此基础上构建四类危险源的风险度量模型。研究表明,危险源的风险度量模型由风险发生的可能性、危险源重要度以及事故导致的损失构成;危险源风险发生的可能性,在具体应用中可以采用各类危险源的不可靠度、故障率等来衡量;危险源在事故中的重要度可以通过分析事故故障树种要素结构重要性获取。根据风险评价的结果,可以对危险源进行分级排序,从而为煤矿事故单危险源的控制提供依据。     

Probabilistic Applications of the Schlömilch Transformation

The Schlömilch transformation, long used by mathematicians for integral evaluation, allows probability mass to be redistributed, thus transforming old distributions to new ones. The transformation is used to introduce some new families of distributions on ⁺. Their general properties are studied, i.e., distributional shape and skewness, moments and inverse moments, hazard function, and random number generation. In general, these distributions are suitable for modeling data where the hazard function initially rises steeply. Their usefulness is illustrated by fitting some human weight data. Besides data fitting, one possible use of the new distributions could be in sensitivity or robustness studies, for example as Bayesian prior distributions.     

Reconstructing the history of forest fire frequency: Identifying hazard rate change points using the bayes information criterion

A problem in model selection, namely the identification of multiple change points for a piece‐wise constant hazard rate, is discussed. A methodology using the Bayes Information Criterion is developed in an overdispersed survival model (with corresponding quasi‐likelihood function). The technique is used to identify changes in the historical frequency of forest fire. It is applied to two datasets derived from time‐since‐fire maps in the Canadian Rockies.     

Estimation and interpretation of models of absolute risk from epidemiologic data,including family-based studies

Absolute risk is the chance that a person with given risk factors and free of the disease of interest at age a will be diagnosed with that disease in the interval (a, a + τ]. Absolute risk is sometimes called cumulative incidence. Absolute risk is a “crude” risk because it is reduced by the chance that the person will die of competing causes of death before developing the disease of interest. Cohort studies admit flexibility in modeling absolute risk, either by allowing covariates to affect the cause-specific relative hazards or to affect the absolute risk itself. An advantage of cause-specific relative risk models is that various data sources can be used to fit the required components. For example, case–control data can be used to estimate relative risk and attributable risk, and these can be combined with registry data on age-specific composite hazard rates for the disease of interest and with national data on competing hazards of mortality to estimate absolute risk. Family-based designs, such as the kin-cohort design and collections of pedigrees with multiple affected individuals can be used to estimate the genotype-specific hazard of disease. Such analyses must be adjusted for ascertainment, and failure to take into account residual familial risk, such as might be induced by unmeasured genetic variants or by unmeasured behavioral or environmental exposures that are correlated within families, can lead to overestimates of mutation-specific absolute risk in the general population.     

Modelling accelerated life test data by using a Bayesian approach

Summary. Because of the high reliability of many modern products, accelerated life tests are becoming widely used to obtain timely information about their time-to-failure distributions. We propose a general class of accelerated life testing models which are motivated by the actual failure process of units from a limited failure population with a positive probability of not failing during the technological lifetime. We demonstrate a Bayesian approach to this problem, using a new class of models with non-monotone hazard rates, the hazard model with potential scope for use far beyond accelerated life testing. Our methods are illustrated with the modelling and analysis of a data set on lifetimes of printed circuit boards under humidity accelerated life testing.     

The Topology of Central Counterparty Clearing Networks and Network Stability

□ This paper derives a measure of central counterparty (CCP) clearing-network risk that is based on the probability that the maximum exposure (the N-th order statistic) of a CCP to an individual general clearing member is large. Our analytical derivation of this probability uses the theory of Laplace asymptotics, which is related to the large deviations theory of rare events. The theory of Laplace asymptotics is an area of applied probability that studies the exponential decay rate of certain probabilities and is often used in the analysis of the tails of probability distributions. We show that the maximum-exposure probability depends on the topology, or structure, of the clearing network. We also derive a CCP's Maximum-Exposure-at-Risk, which provides a metric for evaluating the adequacy of the CCP's and general clearing members’ loss-absorbing financial resources during rare but plausible market conditions. Based on our analysis, we provide insight into how clearing-network structure can affect the maximum-exposure risk of a CCP and, thereby, network stability. We show that the rate function (the exponential decay rate) of the maximum-exposure probability is informative and can be used to compare the relative maximum-exposure risks across different network configurations.     

基于贝叶斯方法的信用评级模型构建与违约概率估计

以贝叶斯方法为基础构建了信用评级和违约概率模型,指出金融机构利用已有评级信息提高债务人信用风险评估准确性的途径,并以单个债务人违约概率度量方法和Merton理论为基础,考虑异质性导致的宏观经济冲击对债务人的不同影响,度量资产组合违约风险。利用相关数据对贝叶斯模型应用给出例证,结果表明贝叶斯方法具有更为灵活的框架和较好的预测能力。     

Monotonicity and comparability in ballistic missile defense systems

A general framework is provided for modeling Ballistic Missile Defense (BMD) systems that are driven by stochastically monotone decreasing discrete time Markov chains, not necessarily temporally homogeneous. Based on a lexicographical ordering of defense vectors, the notions of stochastic monotonicity and comparability for such systems are investigated. Then conditions under which these systems are monotone and comparable are established. For any such BMD system, it is shown that the number of missiles penetrating the defense is decreasing in the likelihood ratio, and the elements of the associated one-step nonhomogeneous transition probability matrix are totally positive of order 2. In addition, the number of missiles penetrating the defense under uniform firing doctrine is shown to be smaller in the reversed hazard rate order than the number of missiles penetrating the defense under random firing doctrine. Particular performance measures are also shown to be monotone and comparable.