统计代写|金融统计代写Financial Statistics代考|AEM 4070

如果你也在 怎样代写金融统计Financial Statistics这个学科遇到相关的难题,请随时右上角联系我们的24/7代写客服。

金融统计是将经济物理学应用于金融市场。它没有采用金融学的规范性根源,而是采用实证主义框架。它包括统计物理学的典范,强调金融市场的突发或集体属性。经验观察到的风格化事实是这种理解金融市场的方法的出发点。

statistics-lab™ 为您的留学生涯保驾护航 在代写金融统计Financial Statistics方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写金融统计Financial Statistics代写方面经验极为丰富,各种代写金融统计Financial Statistics相关的作业也就用不着说。

我们提供的金融统计Financial Statistics及其相关学科的代写,服务范围广, 其中包括但不限于:

  • Statistical Inference 统计推断
  • Statistical Computing 统计计算
  • Advanced Probability Theory 高等概率论
  • Advanced Mathematical Statistics 高等数理统计学
  • (Generalized) Linear Models 广义线性模型
  • Statistical Machine Learning 统计机器学习
  • Longitudinal Data Analysis 纵向数据分析
  • Foundations of Data Science 数据科学基础
统计代写|金融统计代写Financial Statistics代考|AEM 4070

统计代写|金融统计代写Financial Statistics代考|Change of time and operational time

Change of time in random processes such as $R_{s}, V_{s}$, or $N_{s}, s \geqslant 0$, refer to a transformation – tension or compression – of the timeline. This transformation may be either non-random or random; in the latter case it is usually dependent on the trajectory of the process under consideration.

The most well-known transformation is the change of time for Poisson

processes. The simplest version is a homogeneous Poisson process with constant intensity, i.e., process for which the probability of a single jump is constant per unit of time. We can make this probability equal to 1 by stretching or compressing the timeline. For such a Poisson process, called standard, the intervals between time moments of jumps are mutually independent, and the distance between any two consecutive jumps is exponentially distributed with mean 1. The realization of a homogeneous Poisson process with unit intensity can be likened to an alarm clock which buzzes at random at exponentially distributed times and is immediately reset after it.

Given a homogeneous, but not standard, Poisson process $N_{t}$ with the intensity different from unit, but for which the probability of a jump in unit time is equal to $r>0$, we can transform it into a standard process by re-scaling the time axis by $r$, i.e., stretching out the separation between jump points if the process has high intensity, or compressing it if the process has low intensity ${ }^{62}$. Writing this as a formula, we define a new time by setting $\Upsilon_{t}=r t$. The process $N_{r_{t}}$ looks like a realization of the standard Poisson process, even though $N_{t}$ does not.

If the Poisson process is inhomogeneous and its intensity $r_{t}$ depends on time, we can use a similar approach: when the intensity is high, we stretch the original timeline in order to slow down the flow of time. On the contrary, when the intensity is low, we compress the original timeline in order to speed up the flow of time. It is written as
$$
r_{t}=\int_{0}^{t} r_{s} d s .
$$
If $t_{1}, t_{2}, \ldots$ denote the (random) moments of jumps, then $\Upsilon_{t_{2}}, \Upsilon_{t_{2}}, \ldots$ are the transformed moments of jumps. They correspond to the moments of jumps of the standard Poisson process, and the random variables $\Upsilon_{t_{i+1}}-\Upsilon_{t_{i}}$ are independent and all exponentially distributed with mean 1 . This method allows us to reduce the study to the previous case, in which the intensity is constant and the process is a standard Poisson process.

统计代写|金融统计代写Financial Statistics代考|Purpose and origin of Lundberg’s model

Lundberg’s model was created during a time when only individual, essentially static models existed. These dealt with a fixed portfolio and referred to a fixed time in the future. The emergence of Lundberg’s theory was due to the rapid growth of the insurance business, which covered wider fields of applications. To regulate it, a comprehensive model of the insurance company, rather than an individual portfolio, was needed. Lundberg’s model provides a bird’s eye view of the insurance process.

In his memoir [43], entitled “Half a century with probability theory”, H. Cramér gave a brief description of the advancements made in Lundberg’s model, as follows. The net result of the risk business of an insurance company for a period of, say, one year, was considered as the sum of the results for each of the individual policies in this company’s portfolio. Assuming that the risks under all these policies are independent in the aggregate, their connection with the classical central limit theorem of probability theory was made apparent. But the entire insurance process can be considered as an economic system that develops over time and is subject to random fluctuations at each moment of time. Such systems were

considered in those pioneering works which appear as forerunners of the modern theory of stochastic processes today.

According to K. Borch (see [26], p. 439), the approach proposed by F. Lundberg exempts us from the need to consider each individual contract in the portfolio when the probability distribution of the total amount of claim payments is sought. In Lundberg’s approach, it is formed from two distributions, and both of them can be estimated using the business records of the company. From this viewpoint, it is natural to call Lundberg’s theory the collective theory of risk. However, the true innovation of this theory is its dynamic character, which distinguishes this risk theory from the former static risk theory.

统计代写|金融统计代写Financial Statistics代考|Main drawbacks of Lundberg’s model

The most important drawbacks of Lundberg’s model were listed by $\mathrm{K}$. Borch (see [26], p. 450 ) back in 1967 . He wrote that the following assumptions of this model are far-removed from reality:
(i) The stationarity assumption ${ }^{72}$, from which it follows that the nature of the company’s business will never change. This assumption becomes a little more reasonable than it seems at first glance, if we consider operational time.
(ii) The assumption that the probability laws underlying the whole process are fully known.
(iii) The implicit assumption that once made, a decision by management can never be changed.

None of these three items compromises the fundamental value of Lundberg’s approach in which the main idea is to consider the insurance process as the implementation of a large-scale random phenomenon. K. Borch only pointed out the weaknesses, “holes” of the model among which is the assumption of homogeneity and immutability of the probability laws throughout the entire lifetime of the company. It is easy to see that exactly these assumptions of Lundberg’s model contradict to established practice of insurance regulation and management.

H. Cramér noted that problems of this sort have been quite obvious to F. Lundberg, who was concerned about the adequacy of his model. In the historical review [41] of Lundberg’s works, he wrote that
in view of certain misconceptions that have appeared it is, however, necessary to point out that Lundberg repeatedly emphasizes the practical importance of some arrangement which automatically prevents the risk reserve from growing unduly. This point is, in fact, extensively discussed in the papers of 1909,1919 and 1926-28. One possible arrangement proposed to this end is to work with a security factor $\tau=\tau(x)$ which is a decreasing function of the risk reserve $R(t)=x$. Another possibility is to dispose, at predetermined epochs, of part of the risk reserve for bonus distribution. By either method, the growth of the risk reserve may be efficiently controlled. What Lundberg does in this connection is really to work with a rather refined case of what has much later come to be known as a random walk with two barriers.

From certain quarters, the Lundberg’s theory has been declared to be unrealistic because, it is asserted, no limit is imposed on the growth of the risk reserve. In view of what has been said above, it would seem that these critics have not read the author they are criticizing. For a non-Scandinavian author there is, of course, the excuse that most of Lundberg’s works are written in Swedish.

统计代写|金融统计代写Financial Statistics代考|AEM 4070

金融统计代考

统计代写|金融统计代写Financial Statistics代考|Change of time and operational time

随机过程中的时间变化,例如Rs,在s, 或者ñs,s⩾0,指的是时间线的转换——张力或压缩。这种变换可以是非随机的,也可以是随机的;在后一种情况下,它通常取决于所考虑的过程的轨迹。

最著名的变换是泊松的时间变化

过程。最简单的版本是具有恒定强度的齐次泊松过程,即每单位时间单次跳跃的概率是恒定的过程。我们可以通过拉伸或压缩时间线使这个概率等于 1。对于这样一个称为标准的泊松过程,跳跃的时间间隔是相互独立的,并且任何两个连续跳跃之间的距离呈指数分布,均值为1。单位强度的齐次泊松过程的实现可以比喻为闹钟以指数分布的时间随机嗡嗡作响,并在它之后立即重置。

给定一个均匀但非标准的泊松过程ñ吨强度与单位不同,但单位时间内跳跃的概率等于r>0,我们可以通过重新缩放时间轴将其转换为标准过程r,即如果过程强度高,则拉伸跳跃点之间的间隔,如果过程强度低,则压缩它62. 把它写成一个公式,我们通过设置来定义一个新的时间Υ吨=r吨. 过程ñr吨看起来像是标准泊松过程的实现,即使ñ吨才不是。

如果泊松过程是不均匀的并且它的强度r吨取决于时间,我们可以使用类似的方法:当强度高时,我们拉伸原始时间线以减慢时间的流动。相反,当强度较低时,我们压缩原始时间线以加快时间的流动。它写成

r吨=∫0吨rsds.
如果吨1,吨2,…表示跳跃的(随机)时刻,然后Υ吨2,Υ吨2,…是跳跃的变换时刻。它们对应于标准泊松过程的跳跃时刻,以及随机变量Υ吨一世+1−Υ吨一世是独立的,并且均呈指数分布,均值为 1 。这种方法使我们可以将研究减少到以前的情况,其中强度是恒定的,过程是标准的泊松过程。

统计代写|金融统计代写Financial Statistics代考|Purpose and origin of Lundberg’s model

Lundberg 的模型是在只有个别的、基本上是静态的模型存在的时期创建的。这些涉及固定的投资组合,并提到未来的固定时间。伦德伯格理论的出现,是由于保险业务的快速增长,其应用领域覆盖更广。为了对其进行监管,需要一个全面的保险公司模型,而不是单个的投资组合。Lundberg 的模型提供了保险过程的鸟瞰图。

在他的回忆录 [43] 中,题为“半个世纪的概率论”,H. Cramér 简要描述了 Lundberg 模型所取得的进步,如下所示。一家保险公司风险业务在一年内的净结果被认为是该公司投资组合中每个单独保单的结果的总和。假设所有这些政策下的风险总体上是独立的,那么它们与概率论的经典中心极限定理的联系就很明显了。但是整个保险过程可以被认为是一个随着时间而发展的经济系统,并且在每个时刻都受到随机波动的影响。这样的系统是

在那些作为现代随机过程理论的先驱出现的开创性著作中得到了考虑。

根据 K. Borch(参见 [26],第 439 页),当寻求索赔支付总额的概率分布时,F. Lundberg 提出的方法使我们无需考虑投资组合中的每个单独合同。在 Lundberg 的方法中,它由两个分布组成,并且它们都可以使用公司的业务记录来估计。从这个观点来看,将伦德伯格的理论称为风险的集体理论是很自然的。然而,该理论的真正创新之处在于其动态性,这使得该风险理论与以往的静态风险理论有所区别。

统计代写|金融统计代写Financial Statistics代考|Main drawbacks of Lundberg’s model

Lundberg 模型最重要的缺点如下:ķ. Borch(参见 [26],第 450 页)早在 1967 年。他写道,该模型的以下假设与现实相去甚远:
(i)平稳性假设72,由此可见,公司的业务性质永远不会改变。如果我们考虑操作时间,这个假设变得比乍看之下更合理一些。
(ii) 假设整个过程的概率规律是完全已知的。
(iii) 隐含的假设,即一旦做出,管理层的决定永远不会改变。

这三个项目都没有损害 Lundberg 方法的基本价值,该方法的主要思想是将保险过程视为大规模随机现象的实施。K. Borch 只指出了模型的弱点和“漏洞”,其中包括假设公司整个生命周期内的概率规律的同质性和不变性。很容易看出,Lundberg 模型的这些假设与保险监管和管理的既定实践相矛盾。

H. Cramér 指出,这类问题对 F. Lundberg 来说非常明显,他担心他的模型是否充分。在对伦德伯格著作的历史回顾 [41] 中,他写道,
鉴于已经出现的某些误解,有必要指出伦德伯格反复强调某些安排的实际重要性,这种安排会自动防止风险准备金过度增长. 事实上,这一点在 1909、1919 和 1926-28 年的论文中得到了广泛的讨论。为此提出的一种可能的安排是使用安全因素τ=τ(X)这是风险准备金的减函数R(吨)=X. 另一种可能性是在预定时期处置部分风险准备金用于红利分配。无论采用哪种方式,都可以有效控制风险准备金的增长。Lundberg 在这方面所做的实际上是处理一个相当精致的案例,该案例后来被称为具有两个障碍的随机游走。

从某些方面来看,伦德伯格的理论已被宣布为不切实际,因为有人断言,对风险准备金的增长没有任何限制。鉴于以上所说,这些批评者似乎没有阅读他们所批评的作者。当然,对于非斯堪的纳维亚作家来说,伦德伯格的大部分作品都是用瑞典语写成的。

统计代写|金融统计代写Financial Statistics代考 请认准statistics-lab™

统计代写请认准statistics-lab™. statistics-lab™为您的留学生涯保驾护航。

金融工程代写

金融工程是使用数学技术来解决金融问题。金融工程使用计算机科学、统计学、经济学和应用数学领域的工具和知识来解决当前的金融问题,以及设计新的和创新的金融产品。

非参数统计代写

非参数统计指的是一种统计方法,其中不假设数据来自于由少数参数决定的规定模型;这种模型的例子包括正态分布模型和线性回归模型。

广义线性模型代考

广义线性模型(GLM)归属统计学领域,是一种应用灵活的线性回归模型。该模型允许因变量的偏差分布有除了正态分布之外的其它分布。

术语 广义线性模型(GLM)通常是指给定连续和/或分类预测因素的连续响应变量的常规线性回归模型。它包括多元线性回归,以及方差分析和方差分析(仅含固定效应)。

有限元方法代写

有限元方法(FEM)是一种流行的方法,用于数值解决工程和数学建模中出现的微分方程。典型的问题领域包括结构分析、传热、流体流动、质量运输和电磁势等传统领域。

有限元是一种通用的数值方法,用于解决两个或三个空间变量的偏微分方程(即一些边界值问题)。为了解决一个问题,有限元将一个大系统细分为更小、更简单的部分,称为有限元。这是通过在空间维度上的特定空间离散化来实现的,它是通过构建对象的网格来实现的:用于求解的数值域,它有有限数量的点。边界值问题的有限元方法表述最终导致一个代数方程组。该方法在域上对未知函数进行逼近。[1] 然后将模拟这些有限元的简单方程组合成一个更大的方程系统,以模拟整个问题。然后,有限元通过变化微积分使相关的误差函数最小化来逼近一个解决方案。

tatistics-lab作为专业的留学生服务机构,多年来已为美国、英国、加拿大、澳洲等留学热门地的学生提供专业的学术服务,包括但不限于Essay代写,Assignment代写,Dissertation代写,Report代写,小组作业代写,Proposal代写,Paper代写,Presentation代写,计算机作业代写,论文修改和润色,网课代做,exam代考等等。写作范围涵盖高中,本科,研究生等海外留学全阶段,辐射金融,经济学,会计学,审计学,管理学等全球99%专业科目。写作团队既有专业英语母语作者,也有海外名校硕博留学生,每位写作老师都拥有过硬的语言能力,专业的学科背景和学术写作经验。我们承诺100%原创,100%专业,100%准时,100%满意。

随机分析代写


随机微积分是数学的一个分支,对随机过程进行操作。它允许为随机过程的积分定义一个关于随机过程的一致的积分理论。这个领域是由日本数学家伊藤清在第二次世界大战期间创建并开始的。

时间序列分析代写

随机过程,是依赖于参数的一组随机变量的全体,参数通常是时间。 随机变量是随机现象的数量表现,其时间序列是一组按照时间发生先后顺序进行排列的数据点序列。通常一组时间序列的时间间隔为一恒定值(如1秒,5分钟,12小时,7天,1年),因此时间序列可以作为离散时间数据进行分析处理。研究时间序列数据的意义在于现实中,往往需要研究某个事物其随时间发展变化的规律。这就需要通过研究该事物过去发展的历史记录,以得到其自身发展的规律。

回归分析代写

多元回归分析渐进(Multiple Regression Analysis Asymptotics)属于计量经济学领域,主要是一种数学上的统计分析方法,可以分析复杂情况下各影响因素的数学关系,在自然科学、社会和经济学等多个领域内应用广泛。

MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中,其中问题和解决方案以熟悉的数学符号表示。典型用途包括:数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发,包括图形用户界面构建MATLAB 是一个交互式系统,其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题,尤其是那些具有矩阵和向量公式的问题,而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问,这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展,得到了许多用户的投入。在大学环境中,它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域,MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要,工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数(M 文件)的综合集合,可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

R语言代写问卷设计与分析代写
PYTHON代写回归分析与线性模型代写
MATLAB代写方差分析与试验设计代写
STATA代写机器学习/统计学习代写
SPSS代写计量经济学代写
EVIEWS代写时间序列分析代写
EXCEL代写深度学习代写
SQL代写各种数据建模与可视化代写

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