中国城市商业银行盈利能力影响因素分析——基于50家商业银行的微观数据

基于2009-2015年期间国内50家城市商业银行(城商行)的微观数据,采用动态面板数据模型实证检验了资产负债结构及其他宏微观因子对城商业银行盈利能力的影响。研究结论表明:负债及资产结构对城商行的盈利能力具有显著的影响,一般性存款占负债的比重、贷款占资产的比重均与其盈利水平显著正相关;城商行的资产质量及成本控制水平越高,盈利能力也越强;规模越大并不意味着盈利能力越强,那些规模较小的城商行因为聚焦于中小客户群体、具有更高的定价话语权而能够获取更高的盈利;GDP增长率、货币政策等外部宏观环境并不会显著影响城商行的盈利水平。因此,城商行不应追求单纯的规模扩张而应着力强化资产负债统筹管理,并立足自身资源禀赋及市场定位,走差异化、特色化发展道路。     

The Aging Semantic Differential in Mandarin Chinese: Measuring Attitudes toward Older Adults in China

ABSTRACT

The Aging Semantic Differential (ASD) is the most widely used instrument to measure young people’s attitudes towards older adults. This study translated the ASD to Mandarin and examined its psychometric properties. The Mandarin-ASD contains three latent factors (Personality and Mental Health, Societal Participation, and Physical) that have high internal reliability and reasonable discriminate validity. Social work researchers, practitioners and allied professionals may utilize the ASD-Mandarin instrument to measure young people’s attitudes towards older adults in China. We issue a call for a universal-ASD that can be applied across different cultural contexts.     

Semi-parametric segmentation of multiple series using a DP-Lasso strategy

We consider a semi-parametric approach to perform the joint segmentation of multiple series sharing a common functional part. We propose an iterative procedure based on Dynamic Programming for the segmentation part and Lasso estimators for the functional part. Our Lasso procedure, based on the dictionary approach, allows us to both estimate smooth functions and functions with local irregularity, which permits more flexibility than previous proposed methods. This yields to a better estimation of the functional part and improvements in the segmentation. The performance of our method is assessed using simulated data and real data from agriculture and geodetic studies. Our estimation procedure results to be a reliable tool to detect changes and to obtain an interpretable estimation of the functional part of the model in terms of known functions.     

Difference-based matrix perturbation method for semi-parametric regression with multicollinearity

This paper addresses the collinearity problems in semi-parametric linear models. Under the difference-based settings, we introduce a new diagnostic, the difference-based variance inflation factor (DVIF), for detecting the presence of multicollinearity in semi-parametric models. The DVIF is then used to device a difference-based matrix perturbation method for solving the problem. The electricities distribution data set is analyzed, and numerical evidences validate the effectiveness of the proposed method.     

Evaluating modified generalized information criterion in presence of multicollinearity

When there are many explanatory variables in the regression model, there is a chance that some of these are intercorrelated. This is where the problem of multicollinearity creeps in due to which precision and accuracy of the coefficients is marred, and the quest to find the best model becomes tedious. To tackle such a situation, Model selection criteria are applied for selecting the best model that fits the data. Current study focuses on the evaluation of the four unmodified and four modified versions of generalized information criteria—Akaike Information Criterion, Schwarz's Bayes Information Criteria, Hannan-Quinn Information Criterion, and Akaike Information Criterion corrected for small samples. A simulation study using SAS software was carried out in order to compare the unmodified and modified versions of the generalized information criteria and to discover the best version amongst the four modified model selection criteria, for identifying the best model, when the collinearity assumption is violated. For the proposed simulation, two samples of size 50 and 100, for three explanatory variables X₁, X₂, and X₃, are drawn from Normal distribution. Two situations of collinearity violations between X₁ and X₂ are looked into, first when ρ = 0.6 and second when ρ = 0.8. The outcomes of the simulations are displayed in the tables along with visual representations. The results revealed that modified versions of the generalized information criteria are more sensitive in identifying models marred with high multicollinearity as compared to the unmodified generalized information criteria.     

Adaptation of the short version of the Psychological Capital Questionnaire (PCQ-12) into Spanish / Adaptación al español de la versión reducida del Cuestionario de Capital Psicológico (PCQ-12)

Psychological capital (PsyCap) has been a topic of increasing interest in the last decade. However, there is a lack of validated instruments in Spanish to map PsyCap and its consequences for individuals’ well-being. Consequently, the goal of this study is to adapt the 12-item short version of the Psychological Capital Questionnaire (PCQ-12) to the Spanish context by analysing its internal consistency and reliability, factor solution and external validity. Results indicated that the PCQ-12 exhibited good Cronbach’s alpha (.87) and omega coefficient (.93) values in a sample from a vehicle inspection company (n = 798). As expected, results also revealed that a second-order factor structure provided the best fit. Moreover, PsyCap is associated with increased job engagement (β = .579; R²_adjusted = .333), decreased job burnout (β = ?.409; R²_adjusted = .166), and lower psychological distress (β = ?.349; R²_adjusted = .121) (all p < .01), which provided additional support for using the PCQ-12 in the Spanish context. Implications for theory and practice are discussed.     

Optimal Dynamic Order Scheduling under Capacity Constraints Given Demand‐Forecast Evolution

We consider a manufacturer without any frozen periods in production schedules so that it can dynamically update the schedules as the demand forecast evolves over time until the realization of actual demand. The manufacturer has a fixed production capacity in each production period, which impacts the time to start production as well as the production schedules. We develop a dynamic optimization model to analyze the optimal production schedules under capacity constraint and demand‐forecast updating. To model the evolution of demand forecasts, we use both additive and multiplicative versions of the martingale model of forecast evolution. We first derive expressions for the optimal base stock levels for a single‐product model. We find that manufacturers located near their market bases can realize most of their potential profits (i.e., profit made when the capacity is unlimited) by building a very limited amount of capacity. For moderate demand uncertainty, we also show that it is almost impossible for manufacturers to compensate for the increase in supply–demand mismatches resulting from long delivery lead times by increasing capacity, making lead‐time reduction a better alternative than capacity expansion. We then extend the model to a multi‐product case and derive expressions for the optimal production quantities for each product given a shared capacity constraint. Using a two‐product model, we show that the manufacturer should utilize its capacity more in earlier periods when the demand for both products is more positively correlated.     

Progressive Learning

We study a dynamic principal–agent relationship with adverse selection and limited commitment. We show that when the relationship is subject to productivity shocks, the principal may be able to improve her value over time by progressively learning the agent's private information. She may even achieve her first‐best payoff in the long run. The relationship may also exhibit path dependence, with early shocks determining the principal's long‐run value. These findings contrast sharply with the results of the ratchet effect literature, in which the principal persistently obtains low payoffs, giving up substantial informational rents to the agent.     

一种新的金融动态横截面估计方法——基于中国股票市场条件定价模型评估的应用与扩展

大多数资产定价模型常常用静态横截面回归(the static cross-sectional regression)进行定价表现评估,从而投资组合回报率的时间变化性并不能被时变的风险承载或者(和)时变的风险溢价所解释.本文从经济学的角度,运用一种新的金融动态横截面回归(the dynamic cross-sectional regression),首次考察了基于中国股票市场和美国股票市场的条件资产定价模型的定价表现:股票市场投资组合回报率的时变性是否能被时变的风险溢价所解释.本文发现,短期收益反转和流通市值加权市场换手率为条件变量的条件资本资产定价模型和基于消费的条件资本资产定价模型,能更好的解释中国股票投资组合的回报时变性,其时变性主要来自于时变的风险溢价.另外,本文发现一些拥有持续(persistence)和缓慢变化(slow-moving)特性的条件变量更能够解释横截面投资组合的时变回报.     

A Blueprint for Full Collective Flood Risk Estimation: Demonstration for European River Flooding

Floods are a natural hazard evolving in space and time according to meteorological and river basin dynamics, so that a single flood event can affect different regions over the event duration. This physical mechanism introduces spatio‐temporal relationships between flood records and losses at different locations over a given time window that should be taken into account for an effective assessment of the collective flood risk. However, since extreme floods are rare events, the limited number of historical records usually prevents a reliable frequency analysis. To overcome this limit, we move from the analysis of extreme events to the modeling of continuous stream flow records preserving spatio‐temporal correlation structures of the entire process, and making a more efficient use of the information provided by continuous flow records. The approach is based on the dynamic copula framework, which allows for splitting the modeling of spatio‐temporal properties by coupling suitable time series models accounting for temporal dynamics, and multivariate distributions describing spatial dependence. The model is applied to 490 stream flow sequences recorded across 10 of the largest river basins in central and eastern Europe (Danube, Rhine, Elbe, Oder, Waser, Meuse, Rhone, Seine, Loire, and Garonne). Using available proxy data to quantify local flood exposure and vulnerability, we show that the temporal dependence exerts a key role in reproducing interannual persistence, and thus magnitude and frequency of annual proxy flood losses aggregated at a basin‐wide scale, while copulas allow the preservation of the spatial dependence of losses at weekly and annual time scales.