## 金融代写|风险理论代写Risk theory代考|MATH4128

statistics-lab™ 为您的留学生涯保驾护航 在代写风险理论Risk theory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。我们的专家在代写风险理论Risk theory相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (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

## 金融代写|风险理论代写Risk theory代考|Bayes and Empirical Bayes

Let $\boldsymbol{X}=\left(X_1, \ldots, X_n\right)$ be a vector of r.v.s describing the outcome of a statistical experiment. For example, in the insurance context, $n$ can be the number of persons insured for losses due to accidents in the previous year, and $X_i$ the payment made to the $i$ th.

A traditional (frequentists’) model is to assume the $X_i$ to be i.i.d. with a common distribution $F_\theta$ where $\theta$ is an unknown parameter (possibly multidimensional). F.g. in the accident insurance example, one could let $b$ denote the probability that a person has an accident within one year, $b=\mathbb{P}\left(X_i>0\right)$, and one could assume that the cost of the accident has a $\operatorname{gamma}(\alpha, \lambda)$ distribution. Thus the density of $X_i$ is
$$f_{b, \alpha, \lambda}(x)=b \mathbb{1}{x=0}+(1-b) \frac{\lambda^\alpha x^{\alpha-1}}{\Gamma(\alpha)} \mathrm{e}^{-\lambda x_1} \mathbb{1}{x>0}$$
w.r.t. the measure defined as Lebesgue measure $\mathrm{d} x$ on $(0, \infty)$ with an added atom of unit size at $x=0$. Then $\theta=(b, \alpha, \lambda)$, and the conventional statistical procedure would be to compute estimates $\widehat{b}, \widehat{\alpha}, \widehat{\lambda}$ of $b, \alpha, \lambda$. These estimates could then be used as basis for computing first the expectation
$$\mathbb{E}{\widehat{\theta}} X=\mathbb{E}{\widehat{b}, \widehat{\alpha}, \widehat{\lambda}} X=(1-\widehat{b}) \widehat{\alpha} / \widehat{\lambda}$$
of $X$ under the estimated parameters, and next one could use $\mathbb{E}_{\widehat{\theta}} X$ as the net premium and add a loading corresponding to one of the premium rules discussed in Sect. I.3. For example, the expected value principle would lead to the premium
$$p=(1+\eta)(1-\widehat{b}) \widehat{\alpha} / \widehat{\lambda}$$

We now turn to the general implementation of Bayesian ideas in insurance. Here one considers an insured with risk parameter $Z^1$ and an r.v. with distribution $\pi^{(0)}(\cdot)$, with observable past claims $X_1, \ldots, X_n$ and an unobservable claim amount $X_{n+1}$ for year $n+1$. The aim is to assert which (net) premium the insured is to pay in year $n+1$

For a fixed $\zeta$, let $\mu(\zeta)=\mathbb{E}\zeta X{n+1}$, where $\mathbb{E}\zeta[\cdot]=\mathbb{E}[\cdot \mid Z=\zeta]$. The (net) collective premium $H{\mathrm{Coll}}$ is $\mathbb{E} \mu(\boldsymbol{Z})=\mathbb{E} X_{n+1}$. This is the premium we would charge without prior statistics $X_1, \ldots, X_n$ on the insured. The individual premium is $H_{\text {Ind }}=\mathbb{E}\left[X_{n+1} \mid \boldsymbol{Z}\right]=\mu(\boldsymbol{Z})$. This is the ideal net premium in the sense of supplying the complete relevant prior information on the customer. The Bayes premium $H_{\text {Bayes }}$ is defined as $\mathbb{E}\left[\mu(\boldsymbol{Z}) \mid X_1, \ldots, X_n\right]$. That is, $H_{\text {Bayes }}$ is the expected value of $X_{n+1}$ in the posterior distribution.

Note that the individual premium is unobservable because $\boldsymbol{Z}$ is so; the Bayes premium is ‘our best guess of $H_{\text {Ind }}$ based upon the observations’. To make this precise, let $H^$ be another premium rule, that is, a function of $X_1, \ldots, X_n$ and the prior parameters. We then define its loss as $$\ell_{H^}=\mathbb{E}\left[\mu(\boldsymbol{Z})-H^\right]^2=\left|\mu(\boldsymbol{Z})-H^\right|^2$$
where $|X|=\left(\mathbb{E} X^2\right)^{1 / 2}$ is the $L_2$-norm (in obvious notation, we write $\ell_{\text {Coll }}=\ell_{H_{\text {Coll }}}$ etc). In mathematical terms, the optimality property of the Bayes premium is then that it minimizes the quadratic loss:
Theorem 1.3 For any $H^, \ell_{\text {Bayes }} \leq \ell_{H^}$. That is,
$$\mathbb{E}\left(H_{\text {Bayes }}-H_{\text {Ind }}\right)^2 \leq \mathbb{E}\left(H^*-H_{\text {Ind }}\right)^2$$

## 金融代写|风险理论代写Risk theory代考|Bayes and Empirical Bayes

$$f_{b, \alpha, \lambda}(x)=b 1 x=0+(1-b) \frac{\lambda^\alpha x^{\alpha-1}}{\Gamma(\alpha)} \mathrm{e}^{-\lambda x_1} 1 x>0$$
wrt 定义为 Lebesgue 度量的度量 $\mathrm{d} x$ 上 $(0, \infty)$ 添加一个单位大小的原子 $x=0$. 然后 $\theta=(b, \alpha, \lambda)$ ，而传统的统 计程序是计算估计值 $\hat{b}, \widehat{\alpha}, \widehat{\lambda}$ 的 $b, \alpha, \lambda$. 然后可以将这些估计值用作首先计算期望值的基础
$$\mathbb{E} \hat{\theta} X=\mathbb{E} \hat{b}, \widehat{\alpha}, \hat{\lambda} X=(1-\hat{b}) \widehat{\alpha} / \widehat{\lambda}$$

$$p=(1+\eta)(1-\hat{b}) \widehat{\alpha} / \widehat{\lambda}$$

$$\mathbb{E}\left(H_{\text {Bayes }}-H_{\text {Ind }}\right)^2 \leq \mathbb{E}\left(H^*-H_{\text {Ind }}\right)^2$$

## 广义线性模型代考

statistics-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。

## 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。

## 金融代写|风险理论代写Risk theory代考|STAT4901

statistics-lab™ 为您的留学生涯保驾护航 在代写风险理论Risk theory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。我们的专家在代写风险理论Risk theory相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (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

## 金融代写|风险理论代写Risk theory代考|Reinsurance

Reinsurance means that the company (the cedent or first insurer) insures a part of the risk at another insurance company (the reinsurer). The purposes of reinsurance are to reduce risk and/or to reduce the risk volume of the company.

We start by formulating the basic concepts within the framework of a single risk $X \geq 0$. A reinsurance arrangement is then defined in terms of a function $r(x)$ with the property $0 \leq r(x) \leq x$. Here $r(x)$ is the amount of the claim $x$ to be paid by the reinsurer and $s(x)=x-r(x)$ the amount to be paid by the cedent. The function $s(x)$ is referred to as the retention function. The most common examples are the following two:

• Proportional reinsurance $r(x)=(1-\theta) x, s(x)=\theta x$ for some $\theta \in(0,1)$. Also called quota share reinsurance.
• Stop-loss reinsurance $r(x)=(x-b)^{+}$for some $b \in(0, \infty)$, referred to as the retention limit. The retention function is $x \wedge b$.

Concerning terminology, note that in the actuarial literature the stop-loss transform of $F(x)=\mathbb{P}(X \leq x)$ (or, equivalently, of $X)$ is defined as the function
$$b \mapsto \mathbb{E}(X-b)^{+}=\int_b^{\infty}(x-b) F(\mathrm{~d} x)=\int_b^{\infty} \bar{F}(x) \mathrm{d} x$$
(the last equality follows by integration by parts, see formula (A.1.1) in the Appendix). It shows up in a number of different contexts, see e.g. Sect. VIII.2.1, where some of its main properties are listed.

The risk $X$ is often the aggregate claims amount $A=\sum_1^N V_i$ in a certain line of business during one year; one then talks of global reinsurance. However, reinsurance may also be done locally, i.e. at the level of individual claims. Then, if $N$ is the number of claims during the period and $V_1, V_2, \ldots$ their sizes, then the amounts paid by reinsurer, resp. the cedent, are
$$\sum_{i=1}^N r\left(V_i\right), \text { resp. } \sum_{i=1}^N s\left(V_i\right)$$

## 金融代写|风险理论代写Risk theory代考|The Poisson Process

By a (simple) point process $\mathscr{N}$ on a set $\Omega \subseteq \mathbb{R}^d$ we understand a random collection of points in $\Omega$ [simple means that there are no multiple points]. We are almost exclusively concerned with the case $\Omega=[0, \infty)$. The point process can then be specified by the sequence $T_1, T_2, \ldots$ of interarrival times such that the points are $T_1, T_1+T_2, \ldots$ The associated counting process ${N(t)}_{t \geq 0}$ is defined by letting $N(t)$ be the number of points in $[0, t]$. Write
$$\mathscr{N}(s, t]=N(t)-N(s)=#\left{n: s<T_1+\cdots+T_n \leq t\right}$$
for the increment of ${N(t)}$ over $(s, t]$ or equivalently the number of points in $(s, t]$.
Definition 5.2 $\mathscr{N}$ is a Poisson process on $[0, \infty)$ with rate $\lambda$ if ${N(t)}$ has independent increments and $N(t)-N(s)$ has a Poisson $(\lambda(t-s))$ distribution for $s<t$.

Here independence of increments means independence of increments over disjoint intervals.

It is not difficult to extend the reasoning hehind example 1) ahnve to conclude. that for a large insurance portfolio, the number of claims in disjoint time intervals are independent Poisson r.v.s, and so the times of occurrences of claims form a Poisson process. There are, however, different ways to approach the Poisson process. In particular, the infinitesimal view in part (iii) of the following result will prove useful for many of our purposes.

## 金融代写|风险理论代写Risk theory代考|Reinsurance

• 比例再保险 $r(x)=(1-\theta) x, s(x)=\theta x$ 对于一些 $\theta \in(0,1)$. 也称为配额份额再保险。
• 止损再保险 $r(x)=(x-b)^{+}$对于一些 $b \in(0, \infty)$ ，称为保留限制。保留函数为 $x \wedge b$.
关于术语，请注意在精算文献中，止损变换 $F(x)=\mathbb{P}(X \leq x)$ (或者，等效地， $X$ )被定义为函数
$$b \mapsto \mathbb{E}(X-b)^{+}=\int_b^{\infty}(x-b) F(\mathrm{~d} x)=\int_b^{\infty} \bar{F}(x) \mathrm{d} x$$
（最后一个等式后面是分部积分，见附录中的公式 (A.1.1) )。它出现在许多不同的上下文中，例如参见 Sect。 VIII.2.1，其中列出了它的一些主要属性。
风险 $X$ 通常是总索赔额 $A=\sum_1^N V_i$ 一年内从事某项业务；然后有人谈到全球再保险。然而，再保险也可以在当 地进行，即在个人索赔层面。那么，如果 $N$ 是该期间的索赔数量，并且 $V_1, V_2, \ldots$ 他们的规模，然后是再保险公 司支付的金额，resp。分出商是
$$\sum_{i=1}^N r\left(V_i\right), \text { resp. } \sum_{i=1}^N s\left(V_i\right)$$

## 金融代写|风险理论代写Risk theory代考|The Poisson Process

Poisson rvs，因此理赔发生的次数构成一个 Poisson 过程。然而，有不同的方法来处理泊松过程。特别是，以下 结果的 (iii) 部分中的无穷小视图将证明对我们的许多目的有用。

## 广义线性模型代考

statistics-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。

## 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。

## 金融代写|风险理论代写Risk theory代考|STAT553

statistics-lab™ 为您的留学生涯保驾护航 在代写风险理论Risk theory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。我们的专家在代写风险理论Risk theory相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (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

## 金融代写|风险理论代写Risk theory代考|Actuarial Versus Financial Pricing

The last decades have seen the areas of insurance mathematics and mathematical finance coming closer together. One reason is the growing linking of pay-outs of life insurances and pension plans to the current value of financial products, another that certain financial products have been designed especially to be of interest for the insurance industry (see below). Nevertheless, some fundamental differences remain, and the present section aims at explaining some of these, with particular emphasis on the principles for pricing insurance products, resp. financial products.

In insurance, expected values play a major role. For example, let a claim $X \geq 0$ be the amount of money the insurance company has to pay out for a fire insurance on a given house next year (of course, $\mathbb{P}(X=0)$ is close to 1 !). The insurance company then ideally charges $H(X)=\mathbb{E} X$ in premium plus some loading, that is, an extra amount to cover administration costs, profit, risk etc. (different rules for the form of this loading are discussed in Sect. 3). The philosophy behind this is that charging premiums smaller than expected values in the long run results in an overall loss. This is a consequence of the law of large numbers (LLN). In its simplest form it says that if the company faces $n$ i.i.d. claims $X_1, \ldots, X_n$ all distributed as $X$, then the aggregate claim amount $A=X_1+\cdots+X_n$ is approximately $n \mathbb{E} X$ for $n$ large. Therefore, if the premium $H$ is smaller than $\mathbb{E} X$, then with high probability the total premiums $n H$ are not sufficient to cover the total aggregate claims $A$.

This argument carries far beyond this setting of i.i.d. claims, which is of course oversimplified: even in fire insurance, individual houses are different (the area varies, a house may have different types of heating, thatched roof or tiles, etc), and the company typically has many other lines of business such as car insurance, accident insurance, life insurance, etc. Let the claims be $X_1, X_2, \ldots$ Then the asymptotics
$$\frac{X_1+\cdots+X_n}{\mathbb{E} X_1+\cdots+\mathbb{E} X_n} \rightarrow 1$$
holds under weak conditions. For example, the following elementary result is sufficiently general to cover a large number of insurance settings

The standard setting for discussing premium calculation in the actuarial literature is in terms of a single risk $X \geq 0$ and does not involve portfolios, stochastic processes, etc. Here $X$ is an r.v. representing the random payment (possibly 0 ) to be made from the insurance company to the insured. A premium rule is then a $\lfloor 0, \infty)$-valued function $H$ of the distribution of $X$, often written $H(X)$, such that $H(X)$ is the premium to be paid, i.e. the amount for which the company is willing to insure the given risk. From an axiomatic point of view, the concept of premium rules is closely related to that of risk measures, to which we return in Sect. X.1.

The standard premium rules discussed in the literature (not necessarily the same as those used in practice!) are the following:

• The net premium principle $H(X)=\mathbb{E} X$ (also called the equivalence principle). As follows from a suitable version of the CLT that this principle will lead to a substantial loss if many independent risks are insured. This motivates that a loading should be added, as in the next principles:
• The expected value principle $H(X)=(1+\eta) \mathbb{E} X$, where $\eta$ is a specified safety loading. For $\eta=0$, we are back to the net premium principle. A criticism of the expected value principle is that it does not take into account the variability of $X$. This leads to:
• The variance principle $H(X)=\mathbb{E} X+\eta \operatorname{Var}(X)$. A modification (motivated by $\mathbb{E} X$ and $\operatorname{Var}(X)$ not having the same dimension) is
• The standard deviation principle $H(X)=\mathbb{E} X+\eta \sqrt{\operatorname{Var}(X)}$.

## 金融代写|风险理论代写Risk theory代考|Actuarial Versus Financial Pricing

$$\frac{X_1+\cdots+X_n}{\mathbb{E} X_1+\cdots+\mathbb{E} X_n} \rightarrow 1$$

• 净保费原则 $H(X)=\mathbb{E} X$ (也称为等价原则) 。从一个合适的 $\mathrm{CLT}$ 版本可以看出，如果许多独立风险被投 保，这一原则将导致重大损失。这促使应该添加负载，如下面的原则:
• 期望值原则 $H(X)=(1+\eta) \mathbb{E} X$ ，在哪里 $\eta$ 是指定的安全载荷。为了 $\eta=0$ ，我们又回到了净溢价原则。 对期望值原则的一个批评是它没有考虑到 $X$. 这将导致:
• 方差原理 $H(X)=\mathbb{E} X+\eta \operatorname{Var}(X)$. 修改 (动机是 $\mathbb{E} X$ 和 $\operatorname{Var}(X)$ 尺寸不同) 是
• 标准差原则 $H(X)=\mathbb{E} X+\eta \sqrt{\operatorname{Var}(X)}$.

## 广义线性模型代考

statistics-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。

## 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。

## 金融代写|期货期权代写Futures Options代考|MKTG3961

statistics-lab™ 为您的留学生涯保驾护航 在代写期货期权Futures Options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。我们的专家在代写期货期权Futures Options相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (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

## 金融代写|期货期权代写Futures Options代考|Hedging Using Forward Contracts

Suppose that it is May 21, 2020, and ImportCo, a company based in the United States, knows that it will have to pay $£ 10$ million on August 21, 2020, for goods it has purchased from a British supplier. The GBP/USD exchange rate quotes made by a financial institution are shown in Table 1.1. ImportCo could hedge its foreign exchange risk by buying pounds (GBP) from the financial institution in the 3-month forward market at $1.2225$. This would have the effect of fixing the price to be paid to the British exporter at $\$ 12,225,000$. Consider next another U.S. company, which we will refer to as ExportCo, that is exporting goods to the United Kingdom and, on May 21,2020, knows that it will receive$£ 30$million 3 months later. ExportCo can hedge its foreign exchange risk by selling$£ 30$million in the 3-month forward market at an exchange rate of$1.2220$. This would have the effect of locking in the U.S. dollars to be realized for the sterling at$\$36,660,000$.

## 广义线性模型代考

statistics-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。

## 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。

## 金融代写|期货期权代写Futures Options代考|FINE448

statistics-lab™ 为您的留学生涯保驾护航 在代写期货期权Futures Options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。我们的专家在代写期货期权Futures Options相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (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

## 金融代写|期货期权代写Futures Options代考|FORWARD CONTRACTS

A relatively simple derivative is a forward contract. It is an agreement to buy or sell an asset at a certain future time for a certain price. It can be contrasted with a spot contract, which is an agreement to buy or sell an asset almost immediately. A forward contract is traded in the over-the-counter market-usually between two financial institutions or between a financial institution and one of its clients.

One of the parties to a forward contract assumes a long position and agrees to buy the underlying asset on a certain specified future date for a certain specified price. The other party assumes a short position and agrees to sell the asset on the same date for the same price.

Forward contracts on foreign exchange are very popular. Most large banks employ both spot and forward foreign-exchange traders. As we shall see in Chapter 5 , there is a relationship between forward prices, spot prices, and interest rates in the two currencies. Table $1.1$ provides quotes for the exchange rate between the British pound (GBP) and the U.S. dollar (USD) that might be made by a large international bank on May 21 , 2020. The quote is for the number of USD per GBP. The first row indicates that the bank is prepared to buy GBP (also known as sterling) in the spot market (i.e., for virtually immediate delivery) at the rate of $\$ 1.2217$per GBP and sell sterling in the spot market at$\$1.2220$ per GBP. The second, third, and fourth rows indicate that the bank is prepared to buy sterling in 1,3 , and 6 months at $\$ 1.2218, \$1.2220$, and $\$ 1.2224$per GBP, respectively, and to sell sterling in 1,3 , and 6 months at$\$1.2222, \$ 1.2225$, and$\$1.2230$ per GBP, respectively.

Forward contracts can be used to hedge foreign currency risk. Suppose that, on May 21, 2020, the treasurer of a U.S. corporation knows that the corporation will pay $£ 1$ million in 6 months (i.e., on November 21, 2020) and wants to hedge against exchange rate moves. Using the quotes in Table 1.1, the treasurer can agree to buy $£ 1$ million 6 months forward at an exchange rate of $1.2230$. The corporation then has a long forward contract on GBP. It has agreed that on November 21, 2020, it will buy $£ 1$ million from the bank for $\$ 1.2230$million. The bank has a short forward contract on GBP. It has agreed that on November 21,2020 , it will sell$£ 1$million for$\$1.2230$ million. Both sides have made a binding commitment.

## 金融代写|期货期权代写Futures Options代考|Payoffs from Forward Contracts

Consider the position of the corporation in the trade we have just described. What are the possible outcomes? The forward contract obligates the corporation to buy $f 1$ million for $\$ 1,223,000$. If the spot exchange rate rose to, say,$1.3000$, at the end of the 6 months, the forward contract would be worth$\$77,000(=\$ 1,300,000-\$1,223,000)$ to the corporation. It would enable $£ 1$ million to be purchased at an exchange rate of $1.2230$ rather than $1.3000$. Similarly, if the spot exchange rate fell to $1.2000$ at the end of the 6 months, the forward contract would have a negative value to the corporation of $\$ 23,000$because it would lead to the corporation paying$\$23,000$ more than the market price for the sterling.

In general, the payoff from a long position in a forward contract on one unit of an asset is
$$S_T-K$$
where $K$ is the delivery price and $S_T$ is the spot price of the asset at maturity of the contract. This is because the holder of the contract is obligated to buy an asset worth $S_T$ for $K$. Similarly, the payoff from a short position in a forward contract on one unit of an asset is
$$K-S_T$$
These payoffs can be positive or negative. They are illustrated in Figure 1.2. Because it costs nothing to enter into a forward contract, the payoff from the contract is also the trader’s total gain or loss from the contract.

In the example just considered, $K=1.2230$ and the corporation has a long contract. When $S_T=1.3000$, the payoff is $\$ 0.077$per$£ 1$; when$S_T=1.2000$, it is$-\$0.023$ per $£ 1$.

## 金融代写|期货期权代写Futures Options代考|Payoffs from Forward Contracts

$$S_T-K$$

$$K-S_T$$

## 广义线性模型代考

statistics-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。

## 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。

## 金融代写|期货期权代写Futures Options代考|AEM4210

statistics-lab™ 为您的留学生涯保驾护航 在代写期货期权Futures Options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。我们的专家在代写期货期权Futures Options相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (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

A derivatives exchange is a market where individuals and companies trade standardized contracts that have been defined by the exchange. Derivatives exchanges have existed for a long time. The Chicago Board of Trade (CBOT) was established in 1848 to bring farmers and merchants together. Initially its main task was to standardize the quantities and qualities of the grains that were traded. Within a few years, the first futures-type contract was developed. It was known as a to-arrive contract. Speculators soon became interested in the contract and found trading the contract to be an attractive alternative to trading the grain itself. A rival futures exchange, the Chicago Mercantile Exchange (CME), was established in 1919. Now futures exchanges exist all over the world. (See table at the end of the book.) The CME and CBOT have merged to form the CME Group (www.cmegroup.com), which also includes the New York Mercantile Exchange (NYMEX), and the Kansas City Board of Trade $(\mathrm{KCBT})$.

The Chicago Board Options Exchange (CBOE, www.cboe.com) started trading call option contracts on 16 stocks in 1973. Options had traded prior to 1973, but the CBOE succeeded in creating an orderly market with well-defined contracts. Put option contracts started trading on the exchange in 1977. The CBOE now trades options on thousands of stocks and many different stock indices. Like futures, options have proved to be very popular contracts. Many other exchanges throughout the world now trade options. (See table at the end of the book.) The underlying assets include foreign currencies and futures contracts as well as stocks and stock indices.

## 金融代写|期货期权代写Futures Options代考|OVER-THE-COUNTER MARKETS

1. 美国两家金融机构之间的标准化场外衍生品必须尽可能在所谓的掉期执行工具 (SEF) 上进行交易。这些平台类似于交易所，市场参与者可以发布买卖报价，市场参与者可以通过接受其他市场参与者的报价进行交易。
2. 世界大部分地区都要求将 CCP 用于金融机构之间的大多数标准化衍生品交易。
3. 所有交易都必须报告给中央存储库。

## 广义线性模型代考

statistics-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。

## 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。

## 金融代写|衍生品代写Derivatives代考|FIN265

statistics-lab™ 为您的留学生涯保驾护航 在代写衍生品Derivatives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。我们的专家在代写衍生品Derivatives相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (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

One of the subtle characteristics of commodity markets is the difference between traded and non-traded commodities. What is the difference?

An interesting case study that illustrates the key differences is the market for iron ore. Iron ore is used in the production of steel and, combined with steel, represents the world’s second largest commodity bloc by value (ICE, 2009). Macquarie Bank (2013) points out that prior to 2003 the concept of a spot market for the metal did not exist in any meaningful sense. At this time, the traditional buyers were Japanese and Korean steel producers who purchased their metal using annual, fixed price, bilateral contracts with suppliers based mainly in Brazil and Australia. The annual benchmark price typically ran from 1 April-31 March in the following year. Emerging new consumers such as China struggled to purchase the required amount of metal under this market mechanism as the traditional sources of supply could not keep pace with the extra demand. This coincided with a new source of supply from India that was able to react quickly. This led to more ‘one-off’ transactions that resulted in the emergence of a spot market. At the same time commodities that were inputs to the steel making process, which already had developed spot markets, became more volatile. This increased the pressure on iron ore to respond accordingly.

However, the phrase ‘spot’ within the context of commodities can sometimes be applied ambiguously. For example, in certain markets (e.g. gold), spot transactions will have a similar maturity to those seen in traditional financial markets (e.g. trade date plus two good business days). In other instances (e.g. crude oil), delivery is unlikely to occur in such a short time frame. ‘Spot pricing’ could also indicate that the contract is for short-term delivery with prices possibly referencing exchange traded futures prices.
The increase in spot transactions meant that price-reporting companies now disseminated information on physical transactions on a more regular basis. One of the characteristics noted earlier is that commodity markets lack homogeneity and therefore pricing from a single benchmark has become the accepted practice. For iron ore a popular benchmark that has emerged is iron ore with a grade of $62 \%^1$.

## 金融代写|衍生品代写Derivatives代考|FORWARD CONTRACTS

A forward contract will fix the price today for delivery of an asset in the future. Gold sold for spot value will involve the exchange of cash for the metal in two days’ time. However, if the transaction required the delivery in, say, one month’s time, it would be classified as a forward transaction. Forward contracts are negotiated bilaterally between the buyer and seller and are often characterized as being ‘over-the-counter’ (OTC).
The forward transaction represents a contractual commitment and so, if gold is bought forward at USD 1,430.00 an ounce, but the price of gold in the spot market is only USD 1,420.00 at the point of delivery, one cannot walk away from the forward contract and try to buy it in the underlying market. However, it is possible for both parties to mutually agree to terminate the contract early. This could be achieved by agreeing upon a ‘break’ amount, which would reflect the current economic value of the contract. Typically, this is done using a process that is referred to generically as ‘marking to market’. An easier way to understand the issue is to use the concept of an exit price. This is typically taken to be the amount for which an asset could be sold, or a liability settled in an ‘arm’s length’ transaction.

A variation on the standard contract is a floating forward. In this type of transaction, a market participant commits to buy or sell the underlying at a future date, but the applicable price is only set at the point of delivery. The final price that is agreed upon may be based on some pre-agreed formula. For example, the price could be the average of daily spot prices in the month prior to settlement.

## 广义线性模型代考

statistics-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。

## 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。

## 金融代写|衍生品代写Derivatives代考|AEM4210

statistics-lab™ 为您的留学生涯保驾护航 在代写衍生品Derivatives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。我们的专家在代写衍生品Derivatives相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (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

## 金融代写|衍生品代写Derivatives代考|Price reporting agencies

One of the problems faced by various commodity markets over the years is one of price discovery. How does a market participant know if they are achieving fair market value? Consider the following quote from a market participant in 2011 with respect to the metal Rhodium, which was about to be used in the creation of an exchange traded fund aimed at the retail market:
‘With no futures benchmark… all the spot price transparency of molasses… and a risk reward with which only a supremely knowledgeable professional or those wet behind the ears would be comfortable … guess the target audience?’
(Financial Times, 2011)
Since commodities are heterogeneous products, establishing a fair price has always been a challenge for market participants and the main conventions used either involve exchange traded prices (where available) or index values determined by PRAs. IOSCO (2012) defines a PRA as:
‘Publishers and information providers who report prices transacted in physical and some derivative markets and give informed assessment of price levels at distinct points in time’.
They defined a crude oil assessment as:
‘The process of applying a methodology and/or judgement to market data and other information to reach a conclusion about the price of oil’.
In response to IOSCO, one of the PRAs, Platts (2012) described their activities in relation to crude oil as follows:
‘Platts publishes assessments of spot prices for crude oil and refined products in various geographic regions based on a range of factual inputs including information on individual transactions supplied by market participants…Given the heterogeneous nature of the underlying transactions (in terms of trading parties, product quality, location, timing, delivery terms and other factors), the analysis conducted by Platts in determining its published price assessments is essentially qualitative, albeit based on a range of quantitative and factual inputs’.

The commodity trading house Glencore Xstrata describe themselves as follows (Glencore, 2011): ‘(the company) is a leading integrated producer and marketer of commodities, with worldwide activities in the marketing of metals and minerals, energy products and agricultural products and the production, refinement, processing, storage and transport of these products. Glencore operates globally, marketing and distributing physical commodities sourced from third party producers and own production to industrial consumers’. Traditionally, commodity trading houses would have simply bought commodities from producers and then sold them to consumers. However, the definition presented by Glencore Xstrata suggests that over time these entities have evolved to own and operate significant parts of various commodity supply chains. So, the notion of one company being fully integrated along a supply chain is no longer the norm. Indeed, many of the investment banking services highlighted in Table $1.1$ could conceivably be offered by trading houses.

A report by the Financial Times (2013) highlighted the extent of trading house involvement in the market:

= Those trading oil handled more than $15 \mathrm{~m}$ barrels of oil a day.

• The main agricultural trading houses handled about half of the world’s grain and soybean trade flows.
• Two trading houses controlled about $60 \%$ of the zinc market.
• Relatively unknown companies can dominate smaller niche markets such as coffee.
Their growth was attributed to four main factors:
• The economic boom after 2000 in several emerging economies.
• A strategic decision to acquire physical assets.
• Their ability to exploit price arbitrage opportunities because of their increasing presence along the supply chain.
• Consolidation in the period prior to 2000 which reduced competition.

## 金融代写|衍生品代写Derivatives代考|Price reporting agencies

“没有期货基准……糖蜜的所有现货价格透明……以及只有知识渊博的专业人士或耳后湿透的人才会感到舒服的风险奖励……猜猜目标受众？
（金融时报，2011）

“报告实物和某些衍生品市场交易价格并对不同时间点的价格水平进行知情评估的出版商和信息提供者”。

“将方法和/或判断应用于市场数据和其他信息以得出有关石油价格的结论的过程”。
Platts (2012) 对 IOSCO 的回应是，其中一个 PRA 描述了他们与原油相关的活动如下：
‘Platts 根据一系列事实输入（包括市场参与者提供的个别交易信息）发布不同地理区域的原油和成品油现货价格评估……鉴于基础交易的异质性（就交易方而言，产品质量、地点、时间、交货条款和其他因素），普氏在确定其公布的价格评估时进行的分析基本上是定性的，尽管基于一系列定量和事实输入。

= 石油交易量超过15 米每天几桶石油。

• 主要的农产品贸易公司处理了世界粮食和大豆贸易流量的大约一半。
• 两家贸易公司控制了大约60%锌市场。
• 相对不为人知的公司可以主导咖啡等较小的利基市场。
他们的增长归因于四个主要因素：
• 几个新兴经济体 2000 年后的经济繁荣。
• 收购实物资产的战略决策。
• 他们利用价格套利机会的能力是因为他们在供应链中的存在越来越多。
• 2000 年之前的合并减少了竞争。

## 广义线性模型代考

statistics-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。

## 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。

## 金融代写|衍生品代写Derivatives代考|IE2042

statistics-lab™ 为您的留学生涯保驾护航 在代写衍生品Derivatives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。我们的专家在代写衍生品Derivatives相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (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

## 金融代写|衍生品代写Derivatives代考|MARKET OVERVIEW

Figure $1.1$ is a ‘big picture’ overview of commodity markets.
In this diagram there are two main segments, the physical and the financial markets. The diagram was designed without a specific product in mind, but if the reader prefers some context, it may be helpful to think of a popular commodity such as crude oil. Within the physical side of the market there will be three main participants: producers, refiners, and consumers. In addition, trading houses will perform a variety of tasks, which are detailed in a subsequent section. The financial side of the market will incorporate those entities offering financing and risk management services as well as investors seeking to earn a return from the asset class. One aspect that is central to commodities is price discovery, and so the role of futures exchanges is key.

To get a sense of the generic market flows associated outlined in Figure 1.1, consider the following issues faced by market participants:

• Commodities are not homogeneous – it is not particularly helpful to speak in general terms about commodities. For example, the phrase ‘crude oil’ is meaningless as the chemical properties of crude extracted in one location will vary from those in a different location. Trafigura (2016) argues that over 150 types of crude oil are traded worldwide.Commodities need to be transformed into consumer goods – for example, oil needs to be refined to produce gasoline.
• Benchmarks help participants agree on a price for non-homogeneous products – so with respect to crude oil, a particular grade of oil could be priced relative to an agreed benchmark such as a futures contract that references Brent Blend.
• Production and consumption may not take place in the same geographical location – this means that there is a need for transportation. The mode of this transportation can vary for a single commodity. For example, in the USA, crude oil is typically moved by pipeline or train. In other areas such as Europe, sea-borne transport may be more common.
• Consumption and production may not occur simultaneously – a consumer may not need to take immediate delivery of a commodity, therefore storage and inventories are key factors. When there is a geographic element to the issue, it takes time for a commodity to be transported.

## 金融代写|衍生品代写Derivatives代考|MARKET PARTICIPANTS

Market participants are able to manage the respective price risks using derivatives. Although risk management will be considered in greater detail in Chapter 3, it is worth considering some related motivations.
Participants can:

• Avoid risk,
• Retain risk,
• Transfer risk,
• Reduce risk,
• Increase risk.
One of the key roles of derivatives is that they allow different market participants with different risk profiles and objectives to obtain a desired risk exposure. With respect to commodity derivatives the main participants will be physical market participants, price reporting agencies (PRAs), investment banks, commodity trading houses, hedge funds, or ‘real’ money accounts.

Individual product supply chains will be considered in the respective chapter. In general terms, the commodity will need to be produced, refined, and then transformed into a product that can be consumed by the end user. Admittedly this general description does not capture all the different types of commodity supply chains, but the key point is that the participant will typically have some form of price risk at most points along the supply chain

In simple terms, producers will be exposed to falling prices, consumers will be exposed to rising prices, and refiners, processors, and utilities will be exposed to margins (e.g. the income generated from selling gasoline less the cost of buying crude oil). These participants are also faced with a variety of other risks which include:

• Credit, i.e. the unwillingness or inability of a customer to pay their debts.
• Logistical risks surrounding the movement of the commodity.
• Sourcing the right quality of commodity.
• Being able to finance day-to-day operations.

## 金融代写|衍生品代写Derivatives代考|MARKET OVERVIEW

• 商品不是同质的——笼统地谈论商品并不是特别有用。例如，“原油”一词毫无意义，因为在一个地方提取的原油的化学性质与在不同地方提取的原油的化学性质不同。Trafigura (2016) 认为，全球交易的原油种类超过 150 种。商品需要转化为消费品——例如，需要精炼石油以生产汽油。
• 基准帮助参与者就非均质产品的价格达成一致——因此，对于原油，特定等级的石油可以相对于商定的基准进行定价，例如参考布伦特混合物的期货合约。
• 生产和消费可能不在同一个地理位置——这意味着需要运输。这种运输方式可能因单一商品而异。例如，在美国，原油通常通过管道或火车运输。在欧洲等其他地区，海运可能更为普遍。
• 消费和生产可能不会同时发生——消费者可能不需要立即接收商品，因此存储和库存是关键因素。当问题存在地理因素时，运输商品需要时间。

## 金融代写|衍生品代写Derivatives代考|MARKET PARTICIPANTS

• 规避风险，
• 保留风险，
• 转移风险，
• 降低风险，
• 增加风险。
衍生品的关键作用之一是它们允许具有不同风险概况和目标的不同市场参与者获得所需的风险敞口。关于商品衍生品，主要参与者将是实物市场参与者、价格报告机构 (PRA)、投资银行、商品交易公司、对冲基金或“真实”货币账户。

• 信用，即客户不愿意或无力偿还债务。
• 围绕商品流动的物流风险。
• 采购质量合适的商品。
• 能够为日常运营提供资金。

## 广义线性模型代考

statistics-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。

## 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。

## 金融代写|金融实证代写Financial Empirical 代考|Fl4003

statistics-lab™ 为您的留学生涯保驾护航 在代写金融实证Financial Empirical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实证Financial Empirical股权市场金融实证Financial Empirical相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (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

## 金融代写|金融实证代写Financial Empirical 代考|Representation of Solutions

As we already noted in the introduction the solutions $\hat{x}1$ and $\hat{x}_1$ for trend and season are natural polynomial and trigonometric spline functions. For each point in time with an observation $t_k$ polynomial and trigonometric function are changed appropriately by the additional functions which are “cut” there \begin{aligned} &g_1\left(t-t_k\right)=\left(t-t_k\right)^{2 p-1} \ &g_2\left(t-t_k\right)=\sum{j=1}^q a_j\left(b_j \sin \omega_j\left(t-t_k\right)-\left(t-t_k\right) \cos \omega_j\left(t-t_k\right)\right) \end{aligned}
für $t>t_k$ und 0 für $t \leq t_k, k=1, \ldots, n$, mit
$$a_j=\frac{1}{2 \omega_j^2 \prod_{\substack{i=1 \ i \neq j}}^q\left(\omega_i^2-\omega_j^2\right)^2}, \quad b_j=\frac{1}{\omega_j}-4 \omega_j \sum_{\substack{i=1 \ i \neq j}}^q \frac{1}{\omega_i^2-\omega_j^2}, \quad j=1, \ldots, q .$$
To find a solution also the weight function $w_{1 k}$ and $w_{2 k}$ of the representation theorem are chosen as natural polynomial and trigonometric spline functions. Written as vectors and matrices with
$\mathbf{f}_1(t)^{\prime}=\left(\begin{array}{llll}1 & \ldots & t^{p-1}\end{array}\right) \quad \mathbf{g}_1(t)^{\prime}=\left(g_1\left(t-t_1\right) \cdots g_1\left(t-t_n\right)\right)$
$F_1=\left(\begin{array}{cccc}1 & t_1 & \cdots & t_1^{p-1} \ \vdots & \vdots & & \vdots \ 1 & t_n & \ldots & t_n^{p-1}\end{array}\right) \quad G_1=\left(\begin{array}{ccc}g_1\left(t_1-t_1\right) & \cdots & g_1\left(t_1-t_n\right) \ \vdots & & \vdots \ g_1\left(t_n-t_1\right) & \cdots & g_1\left(t_n-t_n\right)\end{array}\right)$

and
$\mathbf{f}_2(t)^{\prime}=\left(\begin{array}{lll}\cos \omega_1 t & \sin \omega_1 t \ldots \cos \omega_q t \sin \omega_q t\end{array}\right)$
$\mathbf{g}_2(t)^{\prime}=\left(g_2\left(t-t_1\right) \ldots g_2\left(t-t_n\right)\right)$
$G_2=\left(\begin{array}{ccc}g_2\left(t_1-t_1\right) & \ldots & g_2\left(t_1-t_n\right) \ \vdots & & \vdots \ g_2\left(t_n-t_1\right) & \ldots & g_2\left(t_n-t_n\right)\end{array}\right) .$
The following representations hold (with real-valued coefficient matrices)

## 金融代写|金融实证代写Financial Empirical 代考|Values of Smoothness of Solutions

The solutions $\hat{x}_1(t)=\mathbf{w}_1(t)^{\prime} \mathbf{y}, \hat{x}_2(t)=\mathbf{w}_2(t)^{\prime} \mathbf{y}$ have smoothness values (cf. measurement of smoothness of weight functions)
\begin{aligned} &Q_1\left(\hat{x}_1\right)=\int_a^b\left|T_1 \hat{x}_1(t)\right|^2 \mathrm{~d} t=\frac{1}{\lambda_1^2} \mathbf{y}^{\prime} A^{\prime} G_1^{\prime} A \mathbf{y}=\frac{1}{\lambda_1} \mathbf{y}^{\prime} W_1^{\prime} A \mathbf{y}=\frac{1}{\lambda_1} \hat{\mathbf{x}}_1^{\prime} \mathbf{u} \geq 0, \ &Q_2\left(\hat{x}_2\right)=\int_a^b\left|T_2 \hat{x}_2(t)\right|^2 \mathrm{~d} t=\frac{1}{\lambda_2^2} \mathbf{y}^{\prime} A^{\prime} G_2^{\prime} A \mathbf{y}=\frac{1}{\lambda_2} \mathbf{y}^{\prime} W_2^{\prime} A \mathbf{y}=\frac{1}{\lambda_2} \hat{\mathbf{x}}_2^{\prime} \hat{\mathbf{u}} \geq 0 . \end{aligned}
If follows that estimations of components in observation points are always nonnegative correlated with the empirical rests $\hat{\mathbf{u}}=\mathbf{y}-\hat{\mathbf{x}}$. Furthermore holds
\begin{aligned} \lambda_1 Q_1\left(\hat{x}_1\right)+\lambda_2 Q_2\left(\hat{x}_2\right) &=\mathbf{y}^{\prime} W^{\prime} A \mathbf{y}=\hat{\mathbf{x}}^{\prime} \hat{\mathbf{u}} \geq 0, \quad W=W_1+W_2, \hat{\mathbf{x}}=\hat{\mathbf{x}}_1+\hat{\mathbf{x}}_2 \ Q\left(\hat{\mathbf{x}}_1, \hat{\mathbf{x}}_2 ; \mathbf{y}\right) &=\left|\mathbf{y}-\hat{\mathbf{x}}_1^{\prime}-\hat{\mathbf{x}}_2\right|^2=|\mathbf{y}-\hat{\mathbf{x}}|^2=|\hat{\mathbf{u}}|^2=\hat{\mathbf{u}}^{\prime} \hat{\mathbf{u}}=\mathbf{y}^{\prime} A^{\prime} A \mathbf{y} \geq 0 \end{aligned}
and therefore for the minimum
\begin{aligned} S\left(\hat{x}_1, \hat{x}_2 ; \mathbf{y}\right) &=\lambda_1 Q_1\left(\hat{x}_1\right)+\lambda_2 Q_2\left(\hat{x}_2\right)+Q\left(\hat{\mathbf{x}}_1, \hat{\mathbf{x}}_2 ; \mathbf{y}\right)=\hat{\mathbf{x}}^{\prime} \mathbf{u}+\hat{\mathbf{u}}^{\prime} \mathbf{u}=\mathbf{y}^{\prime} \mathbf{u}=\mathbf{y}^{\prime} A \mathbf{y} \ &=\mathbf{y}^{\prime} \mathbf{y}-\mathbf{y}^{\prime} W \mathbf{y} \leq \mathbf{y}^{\prime} \mathbf{y} \end{aligned}

## 金融代写|金融实证代写Financial Empirical 代考|Representation of Solutions

$\mathbf{f}_1(t)^{\prime}=\left(\begin{array}{lll}1 & \ldots & t^{p-1}\end{array}\right) \quad \mathbf{g}_1(t)^{\prime}=\left(g_1\left(t-t_1\right) \cdots g_1\left(t-t_n\right)\right)$

$\mathbf{f}_2(t)^{\prime}=\left(\cos \omega_1 t \quad \sin \omega_1 t \ldots \cos \omega_q t \sin \omega_q t\right)$
$\mathbf{g}_2(t)^{\prime}=\left(g_2\left(t-t_1\right) \ldots g_2\left(t-t_n\right)\right)$

## 金融代写|金融实证代写Financial Empirical 代考|Values of Smoothness of Solutions

$$Q_1\left(\hat{x}_1\right)=\int_a^b\left|T_1 \hat{x}_1(t)\right|^2 \mathrm{~d} t=\frac{1}{\lambda_1^2} \mathbf{y}^{\prime} A^{\prime} G_1^{\prime} A \mathbf{y}=\frac{1}{\lambda_1} \mathbf{y}^{\prime} W_1^{\prime} A \mathbf{y}=\frac{1}{\lambda_1} \hat{\mathbf{x}}_1^{\prime} \mathbf{u} \geq 0, \quad Q_2\left(\hat{x}_2\right)=\int_a^b \mid T_2$$

$$\lambda_1 Q_1\left(\hat{x}_1\right)+\lambda_2 Q_2\left(\hat{x}_2\right)=\mathbf{y}^{\prime} W^{\prime} A \mathbf{y}=\hat{\mathbf{x}}^{\prime} \hat{\mathbf{u}} \geq 0, \quad W=W_1+W_2, \hat{\mathbf{x}}=\hat{\mathbf{x}}_1+\hat{\mathbf{x}}_2 Q\left(\hat{\mathbf{x}}_1, \hat{\mathbf{x}}_2 ; \mathbf{y}\right) \quad=1$$

$$S\left(\hat{x}_1, \hat{x}_2 ; \mathbf{y}\right)=\lambda_1 Q_1\left(\hat{x}_1\right)+\lambda_2 Q_2\left(\hat{x}_2\right)+Q\left(\hat{\mathbf{x}}_1, \hat{\mathbf{x}}_2 ; \mathbf{y}\right)=\hat{\mathbf{x}}^{\prime} \mathbf{u}+\hat{\mathbf{u}}^{\prime} \mathbf{u}=\mathbf{y}^{\prime} \mathbf{u}=\mathbf{y}^{\prime} A \mathbf{y} \quad=\mathbf{y}^{\prime} \mathbf{y}-\mathbf{y}^{\prime}$$

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