The endo–exo problem in high frequency financial price fluctuations and rejecting criticality

The endo–exo problem lies at the heart of statistical identification in many fields of science, and is often plagued by spurious strong-and-long memory due to improper treatment of trends, shocks and shifts in the data. A class of models that has shown to be useful in discerning exogenous and endogenous activity is the Hawkes process. This class of point processes has enjoyed great recent popularity and rapid development within the quantitative finance literature, with particular focus on the study of market microstructure and high frequency price fluctuations. We show that there are important lessons from older fields like time series and econometrics that should also be applied in financial point process modelling. In particular, we emphasize the importance of appropriately treating trends and shocks for the identification of the strength and length of memory in the system. We exploit the powerful Expectation Maximization algorithm and objective statistical criteria (BIC) to select the flexibility of the deterministic background intensity. With these methods, we strongly reject the hypothesis that the considered financial markets are critical at univariate and bivariate microstructural levels.     

城市轨道交通相邻多车站动态协同限流研究

为应对城市轨道交通车站高峰期客流压力,限流已经成为常态应对措施,而经常采用的限流策略大多针对单个车站,缺少对相邻多个车站的协同考虑。基于客流在车站间的传播效应,以乘客总延误时间最小为目标,以站台及列车能力为约束条件,构建城市轨道交通相邻多车站协同限流决策模型,同时为实现实时动态控制,引入滚动时域控制方法,优化多车站动态协同限流流程。以北京地铁8号线为例,经过相邻若干车站运营数据进行限流策略计算。结果表明,该模型限流可以在不造成车站服务人数损失的同时,将乘客总延误时间降低21%。     

考虑换乘费用的城市公共交通票价优化

换乘所产生的附加费用,如时间和票价等,导致配流影响因素产生变化,如果沿用传统方式将降低预测精度和可靠性。通过引入换乘次数和方式等因子计算出行等待、乘车、换乘及风险评估预留时间等,定义广义出行费用与计算方法;建立双层规划模型求解最优票价,最后通过算例分析弹性出行需求、换乘费用、票价之间的关系。计算结果表明,换乘费用对出行需求的影响小于票价优化对出行需求的影响,优化票价随换乘费用增加而加速降低,为公共交通票价优化提供研究依据。     

Comparative financing analysis and political economy of noncommunicable diseases

The pandemic of chronic non-communicable diseases (NCDs) poses substantial challenges to the health financing sustainability in high-income and low/middle income countries (LMICs). The aim of this review is to identify the bottle neck inefficiencies in NCDs attributable spending and propose sustainable health financing solutions. The World Health Organization (WHO) introduced the “best buy” concept to scale up the core intervention package against NCDs targeted for LMICs. Population- and individual-based NCD best buy interventions are projected at US$170 billion over 2011–2025. Appropriately designed health financing arrangements can be powerful enablers to scale up the NCD best buys. Rapidly developing emerging nations dominate the landscape of LMICs. Their capability and willingness to invest resources for eradicating NCDs could strengthen WHO outreach efforts in Asia, Africa, and Latin America, much beyond current capacities. There has been a declining trend in international donor aid intended to cope with NCDs over the past decade. There is also a serious misalignment of these resources with the actual needs of recipient countries. Globally, the momentum towards the financing of intersectoral actions is growing, and this presents a cost-effective solution. A budget discrepancy of 10:1 in WHO and multilateral agencies remains in donor aid in favour of communicable diseases compared to NCDs. LMICs are likely to remain a bottleneck of NCDs imposed financing sustainability challenge in the long-run. Catastrophic household health expenditure from out of pocket spending on NCDs could plunge almost 150 million people into poverty worldwide. This epidemiological burden coupled with population ageing presents an exceptionally serious sustainability challenge, even among the richest countries which are members of the Organization for Economic Co-operation and Development (OECD). Strategic and political leadership of WHO and multilateral agencies would likely play essential roles in the struggle that has just begun.     

解非线性电路稳态响应的一种有效算法

本文提供一种计算非线性电路的稳态响应的有效算法,在多频信号激励的强迫振荡电路和耦合振荡电路都可使用。该方法可称为“代换算法”。因为,它每一步的变量都是用残留误差的各个频率分量解关联时不变电路,该电路是用“松弛法”将时变电路转换而来。此算法非常简单而有效,并且可应用于各种非线性电路。     

一种雷达回波数据的实时压缩算法

本文研究了脱靶量测量中的雷达回波数据的实时压缩问题，通过对作匀速直线运动目标的多普勒信号特征的分析，提出了一种基于离散余弦变换（DCT）的多普勒信号的变换域压缩算法，并讨论了根据重构信号由高分辨率谱估计及非线性最小二乘拟合进行脱靶量测量的问题。     

指派问题的改进算法

指派问题是管理技术中的重要内容,指派问题的经典算法就是经典的匈牙利法。针对匈牙利法在处理有诸多指派问题的模型不能进行有效求解的情形,本文提出了匈牙利法的改进算法,并应用改进算法在人员任务分派等实例中进行了有效的求解,得到了优化分配的方案,说明模型算法的有效性和实用性。     

基于PSIM的新型电流源PMSM变频调速研究

车用大功率PMSM变频调速系统,由于行驶环境因素的影响,依靠霍尔传感器或光电编码器进行转速测量的可靠性不高,而无位置传感器技术能较好地解决这一问题。此外,传统的电压源逆变器不适合大功率变频调速,而反阻断IGBT器件不断降低的成本使电流源逆变器的应用成为趋势。现基于Powersim公司开发的PSIM软件,对采用自适应积分法进行转速和转子位置估算的新型电流源PMSM变频调速系统进行仿真研究。     

An extended likelihood framework for modelling discretely observed credit rating transitions

The estimation of the parameters of a continuous-time Markov chain from discrete-time observations, also known as the embedding problem for Markov chains, plays in particular an important role for the modeling of credit rating transitions. This missing data problem boils down to a latent variable setting and thus, maximum likelihood estimation is usually conducted using the expectation-maximization (EM) algorithm. We illustrate that the EM algorithm is likely to get stuck in local maxima of the likelihood function in this specific problem setting and adapt a stochastic approximation simulated annealing scheme (SASEM) as well as a genetic algorithm (GA) to combat this issue. Above that, our main contribution is to extend our method GA by a rejection sampling scheme, which allows one to derive stochastic monotone maximum likelihood estimates in order to obtain proper (non-crossing) multi-year probabilities of default. We advocate the use of this procedure as direct constrained optimization (of the likelihood function) will not be numerically stable due to the large number of side conditions. Furthermore, the monotonicity constraint enables one to combine structural knowledge of the ordinality of credit ratings with real-life data into a statistical estimator, which has a stabilizing effect on far off-diagonal generator matrix elements. We illustrate our methods by Standard and Poor’s credit rating data as well as a simulation study and benchmark our novel procedure against an already existing smoothing algorithm.     

Numerical Maximisation of Likelihood: A Neglected Alternative to EM?

There is by now a long tradition of using the EM algorithm to find maximum‐likelihood estimates (MLEs) when the data are incomplete in any of a wide range of ways, even when the observed‐data likelihood can easily be evaluated and numerical maximisation of that likelihood is available as a conceptually simple route to the MLEs. It is rare in the literature to see numerical maximisation employed if EM is possible. But with excellent general‐purpose numerical optimisers now available free, there is no longer any reason, as a matter of course, to avoid direct numerical maximisation of likelihood. In this tutorial, I present seven examples of models in which numerical maximisation of likelihood appears to have some advantages over the use of EM as a route to MLEs. The mathematical and coding effort is minimal, as there is no need to derive and code the E and M steps, only a likelihood evaluator. In all the examples, the unconstrained optimiser nlm available in R is used, and transformations are used to impose constraints on parameters. I suggest therefore that the following question be asked of proposed new applications of EM: Can the MLEs be found more simply and directly by using a general‐purpose numerical optimiser?