A study on risk retention regulation in asset securitization process

We study the impacts of the recently proposed risk retention regulation for asset securitization, i.e. the issuer has to retain a certain proportion of securitized assets. We also consider the frequently discussed measure to require the issuer disclose certain information of the securitized assets. In a dynamic model with asymmetric information between a risk-averse originating bank and a continuum of risk-averse investors, we find that it is impossible for a flat-rate retention requirement to be optimal for all asset types. Although both risk retention and information disclosure regulations are effective in reducing investors’ informational loss, neither can unconditionally enhance social welfare upon the unregulated case. For both measures, there are associated regulatory cost: risk retention regulation aggravates adverse selection problem because it undermines the channel of informational revelation by the choice of securitization intensity, and information disclosure requirement incurs a signalling cost by distorting banks’ securitization intensity in sending signals. Under an appropriate set of conditions we find that information disclosure requirement complements risk retention regulation when investors are sufficiently risk averse.     

Optimal portfolios under worst-case scenarios

In standard portfolio theories such as Mean–Variance optimization, expected utility theory, rank dependent utility heory, Yaari’s dual theory and cumulative prospect theory, the worst outcomes for optimal strategies occur when the market declines (e.g. during crises), which is at odds with the needs of many investors. Hence, we depart from the traditional settings and study optimal strategies for investors who impose additional constraints on their final wealth in the states corresponding to a stressed financial market. We provide a framework that maintains the stylized features of the SP/A theory while dealing with the goal of security in a more flexible way. Preferences become state-dependent, and we assess the impact of these preferences on trading decisions. We construct optimal strategies explicitly and show how they outperform traditional diversified strategies under worst-case scenarios.     

锅炉压力容器安全阀选型和安装及整定压力需要注意的问题

针对锅炉压力容器安全阀选型和安装及整定压力等存在的问题提出一些需要注意事项。     

浅谈价值工程在地下管线探测项目设计方案评价中的应用

价值工程作为一种将功能与成本、技术与经济相结合而进行技术经济评价的方法,又称为价值分析。本文通过结合地下管线探测项目的具体实例,探讨了在具体的施工过程中,为了在降低工程成本的同时能够有效的保证施工工程的质量和进度,如何采用价值工程的方法选择以及优化施工组织设计的方案。实践证明,在施工项目中应用价值工程的方法不仅能够完善施工方案,还能有效确保施工进度并且能够有效提高利润,最重要的是还能够很好的保证施工质量。     

视频业务识别方法综述

视频用户体验的质量要求给网络传输带来了巨大挑战，未来的虚拟现实/增强现实(Virtual Reality/Augmented Reality，VR/AR) 视频业务则对交互时延提出了更高要求。视频业务识别是网络运营商及视频业务提供商对视频进行优先网络资源分配和自适应速率控制的基本前提。综述了业务识别领域的一般研究方法及其对视频业务的具体应用。首先，总结了每种业务识别方法的工作原理，对比了这些方法应用于视频业务时的优缺点及在准确率和召回率指标上的识别性能;然后，特别介绍了可以提升识别准确率和计算性能的特征选择算法;最后，阐述并分析了高吞吐量网络分类速度不够、无成熟视频业务识别平台和样本分布不均衡等视频业务识别面临的挑战，并针对现有业务识别方法存在的问题，给出了引入移动边缘计算、基于端口的方法串联其他识别方法等可能的解决方案。     

Leadership mindsets: Why new managers fail and what to do about it

It is not a secret that many leadership development programs are not producing intended results, and the majority of first-time managers are ineffective. Even though organizations prioritize leadership development, most companies recognize that their programs need improvement in selecting and developing first-time managers. Perhaps it is time to reconsider the assumptions that these programs are based on. First, while organizations tend to promote high-performing individual contributors, the mindsets of an effective individual contributor do not translate to effective leadership mindsets. Second, leadership development programs generally assume that new managers can change behavior by focusing on skill development. These programs fail to identify, name, and address the underlying mindsets that influence individual action. In this article, I highlight research on leadership and leadership mindsets that can replace the flawed assumptions of underperforming leadership selection and training programs. Building on leadership mindset research, I make recommendations to help organizations better select and train new managers.     

Early Experience and Later Outcomes of Education: Schooling Transition Evidence from Indonesia

This paper investigates the effect of early school experience on later educational attainment. Using the Indonesia Family Life Survey (IFLS), we find that students who repeat a grade at primary school are less likely to progress to junior high school. We also find that grade retention is associated with lower rates of transition from junior high school to senior high school. The relationship persists across years and samples. Meanwhile, the age of students when starting school and the hours they spend there have limited effects on whether they continue their schooling. We also observe that the effects of parental education and household income on the transition of students from primary school to junior high school are weakening. Given the long-term impact of grade retention, policymakers should be cautious when recommending it for underachieving children and should look for alternatives.     

Online adaptive lasso estimation in vector autoregressive models for high dimensional wind power forecasting

Wind power forecasts with lead times of up to a few hours are essential to the optimal and economical operation of power systems and markets. Vector autoregression (VAR) is a framework that has been shown to be well suited to predicting for several wind farms simultaneously by considering the spatio-temporal dependencies in their time series. Lasso penalisation yields sparse models and can avoid overfitting the large numbers of coefficients in higher dimensional settings. However, estimation in VAR models usually does not account for changes in the spatio-temporal wind power dynamics that are related to factors such as seasons or wind farm setup changes, for example. This paper tackles this problem by proposing a time-adaptive lasso estimator and an efficient coordinate descent algorithm for updating the VAR model parameters recursively online. The approach shows good abilities to track changes in the multivariate time series dynamics on simulated data. Furthermore, in two case studies it shows clearly better predictive performances than the non-adaptive lasso VAR and univariate autoregression.     

Optimal contracting with altruism and reciprocity

Motivated by the recent experimental evidence on altruistic behavior, we study a simple principal–agent model where each player cares about other players' utility, and may reciprocate their attitude towards him. We show that, relative to the selfish benchmark, efficiency improves when players are altruistic. Nevertheless, in contrast to what may be expected, an increase in the degree of the agent's altruism as well as a more reciprocal behavior by players has ambiguous effects on efficiency. We also consider the effects of the presence of spiteful players and discuss how monetary transfers between players depend on their degrees of altruism and spitefulness.