Semi-parametric dynamic asymmetric Laplace models for tail risk forecasting,incorporating realized measures

This paper extends the joint Value-at-Risk (VaR) and expected shortfall (ES) quantile regression model of Taylor (2019), by incorporating a realized measure to drive the tail risk dynamics, as a potentially more efficient driver than daily returns. Furthermore, we propose and test a new model for the dynamics of the ES component. Both a maximum likelihood and an adaptive Bayesian Markov chain Monte Carlo method are employed for estimation, the properties of which are compared in a simulation study. The results favour the Bayesian approach, which is employed subsequently in a forecasting study of seven financial market indices. The proposed models are compared to a range of parametric, non-parametric and semi-parametric competitors, including GARCH, realized GARCH, the extreme value theory method and the joint VaR and ES models of Taylor (2019), in terms of the accuracy of one-day-ahead VaR and ES forecasts, over a long forecast sample period that includes the global financial crisis in 2007–2008. The results are favorable for the proposed models incorporating a realized measure, especially when employing the sub-sampled realized variance and the sub-sampled realized range.     

增程式电动汽车产业浅析

新能源汽车是未来汽车产业转型发展的重要方向。我国新能源汽车前期发展取得了突出的成绩,产销量位居世界首位。但目前产业发展正面临新形势,未来一段时期内将由“政策驱动”转向“政策+市场双驱动”。到2021年购置补贴完全退出后的后补贴时代,如何保障新能源汽车产业的健康、可持续发展,值得人们共同思考,开发什么类型的产品才能更好地满足消费者的需求,值得行业企业考虑。     

Stock market volatility forecasting: Do we need high-frequency data?

The general consensus in the volatility forecasting literature is that high-frequency volatility models outperform low-frequency volatility models. However, such a conclusion is reached when low-frequency volatility models are estimated from daily returns. Instead, we study this question considering daily, low-frequency volatility estimators based on open, high, low, and close daily prices. Our data sample consists of 18 stock market indices. We find that high-frequency volatility models tend to outperform low-frequency volatility models only for short-term forecasts. As the forecast horizon increases (up to one month), the difference in forecast accuracy becomes statistically indistinguishable for most market indices. To evaluate the practical implications of our results, we study a simple asset allocation problem. The results reveal that asset allocation based on high-frequency volatility model forecasts does not outperform asset allocation based on low-frequency volatility model forecasts.     

乘积型指数滤波器及其在多目标时延估计中的应用

指数滤波器是一类新构造出来的输出信噪比和目标时延分辨力随指数变化的滤波器，该滤波器在损失一定输出信噪比的前提下可以有效提高目标时延分辨力，从而提高目标时延估计精度,但仅采用单个指数滤波器仍存在输出信噪比和目标时延分辨力均达不到实际需求的情况。在乘积型高阶模糊函数乘积运算的启发下，在指数滤波器的基础上提出了一种新的乘积型指数滤波器，并分析了该乘积型指数滤波器的输出信噪比及目标时延分辨力等性能。仿真实验表明，所提的乘积型指数滤波器在低信噪比情况下可以更有效提高多目标时延估计精度，且算法简单易于实现，适用于背景复杂的多目标参数估计任务。     

Cross‐Functional Integration: Concept Clarification and Scale Development

The centrality of cross‐functional integration (CFI) to supply chain theory and practice has long been recognized. Yet researchers continue to struggle with consistently defining or measuring the CFI construct, thus limiting the utility of CFI research. This research develops (1) a comprehensive definition of CFI that synthesizes previous supply chain research and (2) a valid set of scale items that measure the conceptual domain outlined by this definition. The goal is to build a common foundation for extending knowledge on CFI's antecedents and consequences, and ultimately to improve scholars’ ability to guide a broader practitioner community still struggling to achieve integration in their organizations.     

Threats to the Validity of Logistics and Supply Chain Management Research

To increase the relevance of logistics and supply chain academic research, this paper recommends the development and testing of middle‐range theory and practice‐level theory. Yet, there are a number of research issues that arise when academic researchers test middle‐range and practice‐level theory, both in measuring constructs and in testing theoretical relationships between constructs. Concerning the measurement of constructs, this paper recommends that academic researchers pay more attention to content validity and undertake rigorous processes to ensure content validity. In addition, academic researchers need to more explicitly define constructs as either reflective or formative. If the construct is defined as formative, then the traditional statistical approaches to validate these measurement scales are not recommended. The appropriate use of employing single‐item measures for concrete constructs is discussed. In regard to conducting hypothesis tests, research issues associated with multicollinearity and omitted variable bias are discussed. Relative weight analysis is ideal for testing theoretical models and research hypotheses when survey data are obtained, multicollinearity is present, and there are a large number of independent variables predicting a dependent variable. Thus, relative weight analysis is ideal for testing research hypotheses in logistics and supply chain management.     

An excellent approximation for the m out of n day provision

The m out of n day provision (MooN) of convertible bonds is difficult to handle. To approximating the MooN better, this paper proposes an approach named the conditional range probability (CRP). CRP is the simulated probability of the MooN being reached within a price range at a future time, conditional on today’s price of the underlying, and can be incorporated into any conventional derivatives pricing method. For a purposely designed exotic call option with a 20 out of 30 day provision, CRP under finite difference is found to outperform significantly several existing approaches and produce a mean pricing error of 1% over a wide range of initial underlying prices for the exotic call. The result implies that finite difference utilizing CRP will yield excellent approximating prices for convertible bonds.     

新形势下医院内审范围及内容的思考

本文主要介绍了新形势下医院内审的范围及内容,并针对审计工作范围及内容扩大这一情况,提出了相应的解决对策。     

Calibration Weighting Methods for Complex Surveys

This paper presents a careful investigation of the three popular calibration weighting methods: (i) generalised regression; (ii) generalised exponential tilting and (iii) generalised pseudo empirical likelihood, with a major focus on computational aspects of the methods and some empirical evidences on calibrated weights. We also propose a simple weight trimming method for range‐restricted calibration. The finite sample behaviour of the weights obtained by the three calibration weighting methods and the effectiveness of the proposed weight trimming method are examined through limited simulation studies.     

一种一维距离像的识别算法

随着高分辨率雷达的发展,一维距离像识别已成为雷达目标识别的重要方法之一.为了消除一维距离像的平移,将一维距离像变换到频域,提取其频谱信息作为识别特征,并依据Fisher判据,将高维特征空间数据降到一维空间,提高了算法的实时性.仿真结果表明这是一种方便有效的识别方法.