Risk premiums in asset prices and returns

We review recent research on time-varying risk premiums, including attempts to explain rejections by baillie and others of the unbiasedness hypothesis. Using spot and forward foreign exchange rates we discuss the evidence for time-varying risk premiums, relate it to general equilibrium theories of asset pricing, and describe the artificial economy methodology.     

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), 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.     

基于分位回归的风险保费预测

风险保费预测是非寿险费率厘定的重要组成部分。在传统的分位回归厘定风险保费中,通常假设分位数水平是事先给定的,缺乏一定的客观性。为此,提出了一种应用分位回归厘定风险保费的新方法。基于破产概率确定保单组合的总风险保费,建立个体保单的分位回归模型,并与总风险保费建立等式关系,通过数值方法求解出分位数水平,实现对个体保单风险保费的预测。通过一组实际数据分析表明,该方法具有良好的预测效果。     

时变三因子模型风险系数的动态研究

由Fama和French提出的三因子模型能够较好地解释股票的收益率风险溢价。文章以状态空间模型为框架,将风险因子系数作为状态变量,市场风险溢价作为观测变量,构建时变三因子模型来应对股票市场价格的时变特征。研究结果显示,利用卡尔曼滤波来估计时变风险因子系数,增强了估计结果的准确性与连贯性;风险因子系数变化规律与中国A股市场政策和环境影响相吻合,消除非理性噪声后的时变三因子模型更具有解释力度。     

A Sarmanov family with beta and gamma marginal distributions: an application to the Bayes premium in a collective risk model

In this paper we firstly develop a Sarmanov–Lee bivariate family of distributions with the beta and gamma as marginal distributions. We obtain the linear correlation coefficient showing that, although it is not a strong family of correlation, it can be greater than the value of this coefficient in the Farlie–Gumbel–Morgenstern family. We also determine other measures for this family: the coefficient of median concordance and the relative entropy, which are analyzed by comparison with the case of independence. Secondly, we consider the problem of premium calculation in a Poisson–Lindley and exponential collective risk model, where the Sarmanov–Lee family is used as a structure function. We determine the collective and Bayes premiums whose values are analyzed when independence and dependence between the risk profiles are considered, obtaining that notable variations in premiums values are obtained even when low levels of correlation are considered.     

Claim Dependence Induced by Common Effects in Hierarchical Credibility Models

In the usual credibility model, observations are made of a risk or group of risks selected from a population, and claims are assumed to be independent among different risks. However, there are some problems in practical applications and this assumption may be violated in some situations. Some credibility models allow for one source of claim dependence only, that is, across time for an individual insured risk or a group of homogeneous insured risks. Some other credibility models have been developed on a two-level common effects model that allows for two possible sources of dependence, namely, across time for the same individual risk and between risks. In this paper, we argue for the notion of modeling claim dependence on a three-level common effects model that allows for three possible sources of dependence, namely, across portfolios, across individuals and simultaneously across time within individuals. We also obtain the corresponding credibility premiums hierarchically using the projection method. Then we derive the general hierarchical structure or multi-level credibility premiums for the models with h-level of common effects.     

基于时变参数Copula的ΔCoVaR度量技术

采用基于时变参数Copula的ΔCoVaR度量方法,以动态参数Copula模型描述金融变量间的相依结构、以GARCH类模型描述各金融变量的边际分布,通过构建的联合分布计算ΔCoVaR。利用此方法度量中国大陆与美国、香港的股票市场间的极端风险溢出。实证结果表明:通过此方法计算的ΔCoVaR能同时反映时变波动性与时变相依性,可更灵敏准确地度量危机时的极端风险溢出。     

Knight不确定环境下中国上市银行存款保险定价

提出Knight不确定环境下的银行存款保险定价模型,在该模型下存款保费率不再是一个固定的值,而是一个区间。运用该模型真实测算了中国16家A股上市银行的存款保险费率区间,并利用数值分析的方法,研究不确定性参数对存款保费率区间的重要影响。结果表明:Knight不确定风险对中国银行保费的厘定影响显著,具体表现为随着不确定参数的增大,各银行保险费率区间长度都有增大的趋势,但增大幅度各不相同,因此在进行保费厘定时,不能一概而论,而要"因行而异"。     

基于生存分析法的中国政策性农作物保险费率研究

为探索生存分析法对中国政策性农业保险纯费率厘定的适用性,采用辽宁省盘锦市1987—2008年的水稻、玉米和高粱作物单产数据,分别运用生存分析模型计算出75％产量保障水平的基础风险与巨灾风险概率,进而厘定出三种作物保险纯费率分别为3．0％、3．7％、3．7％。结果表明：该方法可以有效解决国内作物保险纯费率厘定过程中的巨灾风险厘定难题,将对中国政策性农业保险定价与补贴政策提供科学的研究依据。     

Time-varying coefficient models with ARMA–GARCH structures for longitudinal data analysis

Time-varying coefficient models with autoregressive and moving-average–generalized autoregressive conditional heteroscedasticity structure are proposed for examining the time-varying effects of risk factors in longitudinal studies. Compared with existing models in the literature, the proposed models give explicit patterns for the time-varying coefficients. Maximum likelihood and marginal likelihood (based on a Laplace approximation) are used to estimate the parameters in the proposed models. Simulation studies are conducted to evaluate the performance of these two estimation methods, which is measured in terms of the Kullback–Leibler divergence and the root mean square error. The marginal likelihood approach leads to the more accurate parameter estimates, although it is more computationally intensive. The proposed models are applied to the Framingham Heart Study to investigate the time-varying effects of covariates on coronary heart disease incidence. The Bayesian information criterion is used for specifying the time series structures of the coefficients of the risk factors.     

Multistate models in health insurance

We illustrate how multistate Markov and semi-Markov models can be used for the actuarial modeling of health insurance policies, focusing on health insurances that are pursued on a similar technical basis to that of life insurance. In the first part, we give an overview of the basic modeling frameworks that are commonly used and explain the calculation of prospective reserves and net premiums. In the second part, we discuss the biometric insurance risk, focusing on the calculation of implicit safety margins. We present new results on implicit margins in the semi-Markov model and on biometric estimation risk in the Markov model, and we explain why there is a need for future research concerning the systematic biometric risk.     

上海燃料油期货套期保值比率选取的实证研究

通过对上海燃料油期货和现货价格的实证分析,表明期货和现货价格之间存在协整关系,同时价格的波动具有时变性和集聚性特征。考虑这两种特征,建立四个模型计算套期保值比率。结果表明,按照考虑协整关系建立的VECM模型估计的最优套保比率进行套期保值,套期保值效果最好,能使决策者面临的价格风险最小。     

A nonparametric time-varying coefficient model for panel count data

In this research, we describe a nonparametric time-varying coefficient model for the analysis of panel count data. We extend the traditional panel count data models by incorporating B-splines estimates of time-varying coefficients. We show that the proposed model can be implemented using a nonparametric maximum pseudo-likelihood method. We further examine the theoretical properties of the estimators of model parameters. The operational characteristics of the proposed method are evaluated through a simulation study. For illustration, we analyse data from a study of childhood wheezing, and describe the time-varying effect of an inflammatory marker on the risk of wheezing.     

Stock Returns and Expected Business Conditions: Half a Century of Direct Evidence

Using survey data, we characterize directly the impact of expected business conditions on expected excess stock returns. Expected business conditions consistently affect expected excess returns in a counter-cyclical fashion. Moreover, inclusion of expected business conditions in otherwise-standard predictive return regressions substantially reduce the explanatory power of the conventional financial predictors, including the dividend yield, default premium, and term premium, while simultaneously increasing R². Expected business conditions retain predictive power even when including the key nonfinancial predictor, the generalized consumption/wealth ratio. We argue that time-varying expected business conditions likely capture time-varying risk, whereas time-varying consumption/wealth may capture time-varying risk aversion.     

基于马尔可夫转换模型的违约风险溢价预测研究

针对违约风险溢价变化依赖于经济波动状态以及市场、宏观经济变量依赖于经济周期时变因素的阶段,基于马尔可夫转换阶段的具体特征,构建马尔可夫违约风险溢价预测转换模型,并以香港恒生指数信用违约互换波动为例,测算因时变系数波动的指数息差、宏观经济变量等概率,通过实证算例剖析股市、宏观经济变量与违约风险溢价之间的内在联动关系和信用违约风险溢价变化的转换机制,以期实现对违约风险溢价能够进行有效预测,实证仿真结果说明了模型的有效性。     

Comparison of Two Smoothing Methods for Exploring Waning Vaccine Effects

We consider the statistical evaluation and estimation of vaccine efficacy when the protective effect wanes with time. We reanalyse data from a 5-year trial of two oral cholera vaccines in Matlab, Bangladesh. In this field trial, one vaccine appears to confer better initial protection than the other, but neither appears to offer protection for a period longer than about 3 years. Time-dependent vaccine effects are estimated by obtaining smooth estimates of a time-varying relative risk RR( t ) using survival analysis. We compare two approaches based on the Cox model in terms of their strategies for detecting time-varying vaccine effects, and their estimation techniques for obtaining a time-dependent RR( t ) estimate. These methods allow an exploration of time-varying vaccine effects while making minimal parametric assumptions about the functional form of RR( t ) for vaccinated compared wit unvaccinated subjects.     

Bayesian modelling of health insurance losses

The purpose of this paper is to build a model for aggregate losses which constitutes a crucial step in evaluating premiums for health insurance systems. It aims at obtaining the predictive distribution of the aggregate loss within each age class of insured persons over the time horizon involved in planning employing the Bayesian methodology. The model proposed using the Bayesian approach is a generalization of the collective risk model, a commonly used model for analysing risk of an insurance system. Aggregate loss prediction is based on past information on size of loss, number of losses and size of population at risk. In modelling the frequency and severity of losses, the number of losses is assumed to follow a negative binomial distribution, individual loss sizes are independent and identically distributed exponential random variables, while the number of insured persons in a finite number of possible age groups is assumed to follow the multinomial distribution. Prediction of aggregate losses is based on the Gibbs sampling algorithm which incorporates the missing data approach.     

连续贝塔、非连续贝塔与股票风险溢酬

本文利用高频数据将传统的CAPM贝塔分解为连续贝塔和非连续贝塔（跳跃贝塔和隔夜贝塔），并在此基础上进一步考虑正向市场和负向市场的非对称性，将跳跃贝塔又细分为正向跳跃贝塔和负向跳跃贝塔，以探讨不同类型系统性风险的特征差异及其所对应的风险溢酬。实证结果表明，个股对市场发生的非连续变动比连续变动更加敏感，投资者对市场发生的负向跳跃比正向跳跃反应更加强烈；中国股票市场上的系统性非连续风险溢酬（跳跃风险溢酬和隔夜风险溢酬）显著为正，但系统性连续风险并没有得到定价；其中，跳跃风险溢酬则主要来源于对系统性负向跳跃风险的补偿，而正向跳跃风险对股票横截面收益率没有显著的影响。     

中国金融风险周期监测与央行货币政策非对称性效果识别

本文采用时变参数因子增广向量自回归模型（TVP-FAVAR）,并基于动态模型平均法测度了金融子系统变量的时变权重,通过加权计算得到我国金融风险周期指数（FRI）。然后改进构建了分区制货币政策模型系统,并采用逻辑平滑转换向量自回归模型（LST-VAR）研究了高低区制下,价格型和数量型货币政策对金融风险的传导效应,在此基础上,设计并测度了我国货币政策的时滞效应。研究发现,我国金融风险具有明显的两区制特征,样本区间内FRI大多时期处于低风险区制,而高风险区制的时间段与国内外重大金融风险事件相吻合。两种紧缩的货币政策均有利于抑制金融风险及其波动,且在高风险区制的抑制效应更为显著。对于货币政策时滞效应,数量型货币政策对金融风险的抑制作用大于价格型货币政策,但2008、2012和2015年三个高金融风险期的时滞效应均较小。最后,根据研究结果提出相应政策建议。