## 金融代写|金融衍生品代写Financial derivatives代考|ECON6042

statistics-lab™ 为您的留学生涯保驾护航 在代写金融衍生品Financial 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。我们的专家在代写金融衍生品Financial derivatives代写方面经验极为丰富，各种代写金融衍生品Financial 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|金融衍生品代写Financial derivatives代考|OTC Derivatives Documentation

The financial institutions offering derivative products and services control and monitor their counterparty risk with the trading counterparty. Some products such as a deposit or a structured note imply one-way counterparty risk where the end investor takes the counterparty risk of the financial institution but the financial institution has no risk from the end investor. However, in some other products such as forward and swap, both parties are taking counterparty risk. The growth of OTC derivatives business prompted the standardization for the contract terms as well as the counterparty credit risk management which are widely used by the institutions and corporates actively involved in financial market.

ISDA (International Swaps and Derivatives Association) Master Agreement, initially developed in the 1980 s to cover the IRS and currency swaps, has been progressively updated to include the derivatives such as forward, swap and option linked to equities, commodities and funds. It sets out standard terms applied to all trades between the two parties.

In general, two parties set up their trading relationship for OTC derivatives by negotiating the applicability and eventual adjustment of the standard terms. The document containing the agreed terms is the ISDA Master Agreement signed by both parties.

CSA (Credit Support Annex) is the document for credit support (i.e. collateral) for derivative transactions. It defines the acceptable collaterals with the “haircuts”. In particular, it defines the “Threshold” which is the consolidated MtM level of all the trades to trigger the margin call. The “Independent Amount (IA)” is the initial margin (collateral) required by one party (usually the dealer) to the other party (usually the end user) for mitigating the counterparty risk linked to an OTC transaction. It is returned only after the termination of the transaction. Its level depends on the volatility of the mark-to-market value of the trade as well as the credit worthiness of the counterparty. During the life of the trade, “variation margin” will be exchanged according to its mark-to-market.

The institutions and corporates actively transacting derivatives usually establish an ISDA/CSA Master Agreement with their counterparties. Normally, the credit agreement between two financial institutions is a two-way CSA in which both parties may post margins for their OTC trades. Between a financial institution and a corporate (especially the small ones), the credit agreement may be a one-way CSA, meaning that only the corporate posts margins to the financial institution. Under a master agreement (ISDA/CSA or any bespoke master agreement), the specific terms and conditions of each OTC derivative trade will take a short form called term sheet or transaction supplement. The individual investors, small corporates and other nonactive entities involved in financial market normally trade with financial institutions with a hespoke agreement or a long form confirmation which contains all terms and conditions for each trade.

## 金融代写|金融衍生品代写Financial derivatives代考|Securities Borrowing & Lending and Repo

A repurchase agreement is a contract for the sale of a security (e.g. stock or bond) with a commitment by the seller to buy the same security back from the buyer at a specified price at a future date. During the tenor of the trade, the seller (also called the lender) of the security surrenders the legal ownership of the security. There are two activities based on the repurchase agreement: Securities Lending and Repo.

Securities Borrowing \& Lending $(S B L)$ transaction allows the lender to lend securities to the borrower on either “Open” (i.e. anytime callable) or “Term” (a fixed tenor) basis. Upon the trade termination, the securities will be returned to the lender. The borrower posts collateral with daily adjustment and pays fees to the lender. The fee rate depends on the borrow supply/demand for the underlying security. The eligible collateral can be cash or other securities negotiated by the parties. The cash collateral level is usually $\sim 105 \%$ of the latest closing price of the security. The International Securities Lending Association has developed a standard agreement called Global Master Securities Lending Agreement (GMSLA) which is followed by most of the institutions. The motivation for the borrower includes short position recovering, hedging of derivatives, corporate action arbitrage, etc.

In a sale and repurchase agreement (Repo), one counterparty (the repo seller) is borrowing money and providing collateral (mostly fixed-income assets) for the loan. See Fig. 1.1 for reference. The seller gains access to funds at lower funding costs than are typically available elsewhere as the loan is collateralized. The collateral eligibility and haircuts are negotiable between the repo counterparties. The standard agreement for Repo is Global Master Repurchase Agreement (GMRA), published by the International Capital Market Association (ICMA). A Reverse Repo is the opposite transaction seen by the other counterparty of the Repo trade. Some central banks use Repo/Reverse Repo operations to regulate the money supply in the financial system.

If the collateral is held at a third party, usually a custodian bank or an international central securities depository, the transaction is call a Tri-Party Repo or Tri-Party Securities Lending. The third party will provide services such as the valuation and adjustment of the collateral. The risk in a Tri-Party Repo transaction is the correlation of the default probability of the counterparty and the value of the collateral in custody.

Although most Repo activities take place on the OTC market, there exists Stock Exchange Repo (e.g. Shanghai Stock Exchange Repo) whereby the exchange determines the collateral pool and haircuts, standardizes the contract features such as size and tenor, and facilitates clearing and pledge of collateral.

## 金融代写|金融衍生品代写Financial derivatives代考|OTC Derivatives Documentation

ISDA（国际掉期和衍生品协会）主协议最初于 1980 年代制定，涵盖 IRS 和货币掉期，现已逐步更新以包括与股票、商品和基金挂钩的远期、掉期和期权等衍生品。它规定了适用于双方之间所有交易的标准条款。

CSA（Credit Support Annex）是为衍生品交易提供信用支持（即抵押品）的文件。它用“折扣”定义了可接受的抵押品。特别是，它定义了“阈值”，即触发追加保证金的所有交易的综合 MtM 水平。“独立金额 (IA)”是一方（通常是交易商）向另一方（通常是最终用户）要求的初始保证金（抵押品），以减轻与场外交易相关的交易对手风险。它仅在交易终止后返回。其水平取决于交易按市值计价的波动性以及交易对手的信用度。在交易期间，“变动保证金”将根据其市值进行兑换。

## 有限元方法代写

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。

## 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|金融衍生品代写Financial derivatives代考|FINE448

statistics-lab™ 为您的留学生涯保驾护航 在代写金融衍生品Financial 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。我们的专家在代写金融衍生品Financial derivatives代写方面经验极为丰富，各种代写金融衍生品Financial 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|金融衍生品代写Financial derivatives代考|Financial Markets

A financial market designates the aggregate of participants, organizations and facilities in which people trade financial securities (e.g. stocks and bonds), currencies and commodities at prices that reflect supply and demand. The participants include financial institutions (such as banks, insurance companies, pension funds, mutual funds, hedge funds), individual investors and corporations.
The financial market can be detailed by the type of services it offers:
Capital markets: which provide financing through the issuance of shares and debts, and enable the subsequent trading thereof. Capital markets include debt and equity markets.

• Equity market (also known as Stock market) allows the participants to buy and sell stocks of (publicly traded) companies. The value of a stock reflects the view about the expected dividend payments, future earnings, and resources that the company will control.
• Debt market (or Fixed-income market) includes bond market which deals in government, corporate and other bonds for long term financing, and money market for short term (up to 1 year) debt securities such as bank deposits, treasury bills, certificates of deposit, commercial papers, etc.
Foreign exchange market: where currencies are bought and sold.
Commodity market: where commodities such as precious metals, industrial metals, energy products and agricultural products are traded. Futures contracts are the most convenient instruments for commodities trading activities. A futures contract may be sold out before the commodity is due to be delivered.
Derivatives market: where futures, swaps, options and other derivatives are transacted.
The financial market can also be classified with other criteria, such as
Primary market: where new issues are first sold through IPOs (Initial Public Offerings). The primary market business for debts and stocks is covered respectively by DCM (Debt Capital Market) and ECM (Equity Capital Market) divisions of the Corporate Finance entity in the investment banks.
Secondary market: for all subsequent trading after IPO between market participants. It constitutes the support for the financial products for investment and risk management.

## 金融代写|金融衍生品代写Financial derivatives代考|Centralized Clearing

As an OTC derivative involves potential payments between the parties in the future, the counterparty risk that one party does not pay as obligated in the contract can not be neglected. To mitigate the counterparty risk, the long and short parties of a derivative contract may transact with the “Central Clearing House” which covers the risk by a collateral deposit system known as the margining system.

For illustration purpose, we take the case of futures contracts transacted at a Futures Exchange serving as the central clearing house.

• The buyer and seller should have their “margin account” in place before trading futures.
• At the contract inception, both parties will deposit an “initial margin”, fixed by the exchange according to the type and price of futures, as collateral which are typically cash or government bonds.
• At the end of each trading session, each party will have their margin account debited or credited for the daily P/L (Profit and Loss, or PnL).
• A minimum margin level called “maintenance margin” is required for every margin account. If the account value is below this level, a “margin call” will be issued for bringing back the account to the level of initial margin.

## 金融代写|金融衍生品代写Financial derivatives代考|Financial Markets

• 股票市场（也称为股票市场）允许参与者买卖（公开交易的）公司的股票。股票的价值反映了对预期股息支付、未来收益和公司将控制的资源的看法。
• 债务市场（或固定收益市场）包括交易政府、公司和其他长期融资债券的债券市场，以及短期（最长 1 年）债务证券的货币市场，例如银行存款、国库券、凭证存款、商业票据等。
外汇市场：买卖货币的地方。
商品市场：交易贵金属、工业金属、能源产品和农产品等商品的市场。期货合约是商品交易活动最方便的工具。期货合约可能会在商品到期交付之前售罄。
衍生品市场：交易期货、掉期、期权和其他衍生品的市场。
金融市场也可以根据其他标准进行分类，例如
一级市场：新发行的股票首先通过 IPO（首次公开募股）出售。债权和股票的一级市场业务分别由投资银行企业金融实体的DCM（债务资本市场）和ECM（股权资本市场）部门负责。
二级市场：适用于市场参与者之间首次公开募股后的所有后续交易。它构成了对投资和风险管理的金融产品的支持。

## 金融代写|金融衍生品代写Financial derivatives代考|Centralized Clearing

• 买方和卖方在交易期货之前应该有他们的“保证金账户”。
• 在合约开始时，双方将存入交易所根据期货品种和价格确定的“初始保证金”作为抵押品，通常为现金或国债。
• 在每个交易时段结束时，每一方都将在其保证金账户中扣除或记入每日损益（损益或 PnL）。
• 每个保证金账户都需要一个称为“维持保证金”的最低保证金水平。如果账户价值低于此水平，将发出“追加保证金通知”以将账户恢复至初始保证金水平。

## 有限元方法代写

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。

## 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|金融衍生品代写Financial derivatives代考|AEM4210

statistics-lab™ 为您的留学生涯保驾护航 在代写金融衍生品Financial 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。我们的专家在代写金融衍生品Financial derivatives代写方面经验极为丰富，各种代写金融衍生品Financial 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|金融衍生品代写Financial derivatives代考|Investment Returns and Risks

The investment return of a non-dividend paying asset for the period between time $s$ and time $t(s<t)$, is defined as:
$R=\frac{P_t-P_s}{P_s} \quad$ or $\quad R=\frac{P_t}{P_s}-1, \quad$ where $P_t$ is the price of the asset at time $t$.

The return rate is an annualized concept in general. There are different types of return rates. For instance, the IRR (Internal Rate of Return) is the value of $r$ which makes the following equality:
$P_t=P_s(1+r)^\tau$, where $\tau$ represents the number of years for the period $[s, t]$.
Usual measures for the rate of return of an asset or a portfolio are
Price return: it is measured by the portfolio’s value at the beginning and the end of the period. The dividend payments during the period are ignored.
Total return: it is obtained with all the dividends re-invested back into the assets of the portfolio with the same proportion. It represents the return of a fully funded portfolio.
Excess return: It is defined as the portfolio’s total return minus the financing cost or a relevant interest rate reference. It represents the return of a self-financed portfolio.

## 有限元方法代写

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。

## 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|期权定价理论代写Option Pricing Theory代考|MATH485

statistics-lab™ 为您的留学生涯保驾护航 在代写期权定价理论Option Pricing 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。我们的专家在代写期权定价理论Option Pricing Theory代写方面经验极为丰富，各种代写期权定价理论Option Pricing 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|期权定价理论代写Option Pricing Theory代考|Complete models versus incomplete models

From (1.13), the market model (1.1) is complete if there exists a unique $\mathcal{F}{t^{-}}$ adapted vector $\lambda_t \in \mathbb{R}^d$ such that $$\forall i \in \text { asset, } \quad b_i\left(t, X_t\right)-r_t X_t^i=\sum{j=1}^d \sigma_{i, j}\left(t, X_t\right) \lambda_t^j$$
The unique ELMM $\mathbb{Q}$ is then given by
$$\frac{d \mathbb{Q}}{\left.d \mathbb{P}^{h i s t}\right|{\left.\right|{\mathcal{F}T}}} \equiv Y_T(-\lambda ; 1)=\prod{j=1}^d e^{-\int_0^T \lambda_t^j d W_t^j-\frac{1}{2} \int_0^T\left(\lambda_t^j\right)^2 d t}$$
The unique arbitrage-free price is
$$\mathcal{B}t\left(F_T\right)=\mathcal{S}_t\left(F_T\right)=\mathbb{E}^{\mathbb{Q}}\left[D{t T} F_T \mid \mathcal{F}t\right]$$ An inspection of (1.14) reveals that if the market is complete, then the rank of $\sigma\left(t, X_t\right)$ is equal to $d$ a.s. (which implies that #assets $\geq d$ ). In the case where #assets ${i, j}\left(t, X_t\right)\right){i \in \text { asset }, 1 \leq j \leq d}$ is invertible, then the market is complete. So, provided the volatility matrix $\sigma{i, j}\left(t, X_t\right)$ is correctly estimated, there is a unique arbitrage-free price.

Examples of complete models that are commonly used by practitioners include Dupire’s local volatility model [95]; Libor market models with local volatilities, e.g., BGM with deterministic volatilities [67]; and Markov functional models [138] (see also [11], Chapter 2).

Common examples of incomplete models are stochastic volatility models (in short SVMs). Here #assets $<d$. An example of stochastic volatility model is the double lognormal SVM, which has attracted the attention of practitioners in equity markets $[113,137]$. The dynamics of the underlying, denoted by $X_t$, reads under a risk-neutral measure $\mathbb{Q}^0 \sim \mathbb{P}^{\text {hist }}$ as
with $\chi^2=1-\rho_{\mathrm{XV}^0}^2-\frac{\left(\rho_{\mathrm{XV}}-\rho \rho_{\mathrm{XV}}\right)^2}{1-\rho^2}$ and $W_t^1, W_t^2, W_t^3$, three uncorrelated standard $\mathbb{Q}^0$-Brownian motions. $V_t$ is the instantaneous variance, and $V_t^0$ plays the role of a moving long-term average value for $V_t$. Neither $V_t$ nor $V_l^0$ are tradable instruments.

## 金融代写|期权定价理论代写Option Pricing Theory代考|Pricing in practice

In practice, the seller’s price at time $t$ is computed by picking out a particular ELMM $\mathbb{Q}$ :
$$u_t \equiv \mathbb{E}^{\mathbb{Q}}\left[D_{t T} F_T \mid \mathcal{F}_t\right]$$
Under this measure $\mathbb{Q}$, the drift for an asset $X^i$ is fixed to $b_i\left(t, X_t\right) \equiv r_t X_t^i$ (see Remark 1.1). In an incomplete market, $\mathbb{Q}$ does not necessary achieve the supremum in Theorem 1.3, and we lose the superhedging strategy paradigm. Selling options becomes a risky business. However, it seems that the idea of a “true price” (based on a “true model”) is still vivid in the community of structurers and sales people (see the quote at the beginning of this chapter). In practice, picking a particular ELMM simplifies a lot the pricing problem: it becomes a linear problem, i.e., the price of the (European) payoff $F_T^1+F_T^2$ equals the sum of the prices of the (European) payoffs $F_T^1$ and $F_T^2$.

We will always assume that there exists a (deterministic) function $r$ such that $r_t=r\left(t, X_t\right)$. Then
$$D_{t_1 t_2}=\exp \left(-\int_{t_1}^{t_2} r\left(s, X_s\right) d s\right)$$
If there exists $g$ such that $F_T=g\left(X_T\right)$, we speak of a vanilla option. In such a case, by the Markov property of $X$,
$$u_t=\mathbb{E}^{\mathbb{Q}}\left[\exp \left(-\int_t^T r\left(s, X_s\right) d s\right) g\left(X_T\right) \mid \mathcal{F}_t\right] \equiv u\left(t, X_t\right)$$
is a function $u$ of $\left(t, X_t\right)$. Below, we recall that $u(t, x)$ is a solution to a linear second order parabolic PDE, the so-called Black-Scholes pricing PDE.

# 期权理论代写

## 金融代写|期权定价理论代写Option Pricing Theory代考|Complete models versus incomplete models

$$\forall i \in \text { asset, } \quad b_i\left(t, X_t\right)-r_t X_t^i=\sum j=1^d \sigma_{i, j}\left(t, X_t\right) \lambda_t^j$$

$$\frac{d \mathbb{Q}}{d \mathbb{P}^{p i s t}|| \mathcal{F} T} \equiv Y_T(-\lambda ; 1)=\prod j=1^d e^{-\int_0^T \lambda_t^j d W_t^j-\frac{1}{2} \int_0^T\left(\lambda_t^j\right)^2 d t}$$

$$\mathcal{B} t\left(F_T\right)=\mathcal{S}t\left(F_T\right)=\mathbb{E}^{\mathbb{Q}}\left[D t T F_T \mid \mathcal{F} t\right]$$ 对 (1.14) 的检查表明，如果市场是完备的，那么 $\sigma\left(t, X_t\right)$ 等于 $d$ 作为（这意味着#assets $\geq d$ ). 在#assets 供波动率矩阵 $\sigma i, j\left(t, X_t\right)$ 被正确估计，存在唯一的无套利价格。 从业者常用的完整模型示例包括 Dupire 的局部波动率模型 [95]；具有局部波动率的 Libor 市场模型，例 如具有确定性波动率的 BGM [67]；和马尔可夫函数模型 [138]（另见 [11]，第 2 章)。 不完整模型的常见示例是随机波动率模型 (简称 SVM) 。这里#assets ${\mathrm{XV}^0}^2-\frac{\left(\rho_{\mathrm{XV}}-\rho \rho_{\mathrm{XV}}\right)^2}{1-\rho^2}$ 和 $W_t^1, W_t^2, W_t^3$ ，三个不相关的标准 $\mathbb{Q}^0$-布朗运动。 $V_t$ 是瞬时方 差，并且 $V_t^0$ 起移动长期平均值的作用 $V_t$. 两者都不 $V_t$ 也不 $V_l^0$ 是可交易的工具。

## 金融代写|期权定价理论代写Option Pricing Theory代考|Pricing in practice

$$u_t \equiv \mathbb{E}^{\mathbb{Q}}\left[D_{t T} F_T \mid \mathcal{F}t\right]$$ 在这项措施下 $\mathbb{Q}$ ，资产的漂移 $X^i$ 固定为 $b_i\left(t, X_t\right) \equiv r_t X_t^i$ (见备注 1.1)。在不完全的市场中， $\mathbb{Q}$ 不一定 达到定理 $1.3$ 中的上确界，我们就失去了超级对冲策略范式。出售期权成为一项有风险的业务。然而，“真 实价格”（基于“真实模型”）的想法似乎在架构师和销售人员群体中仍然很生动（参见本章开头的引述）。 在实践中，选择一个特定的 ELMM 大大简化了定价问题：它变成了一个线性问题，即（欧洲）收益的价格 $F_T^1+F_T^2$ 等于 (欧洲) 收益的价格总和 $F_T^1$ 和 $F_T^2$. 我们将始终假设存在一个 (确定性的) 函数 $r$ 这样 $r_t=r\left(t, X_t\right)$. 然后 $$D{t_1 t_2}=\exp \left(-\int_{t_1}^{t_2} r\left(s, X_s\right) d s\right)$$

$$u_t=\mathbb{E}^{\mathbb{Q}}\left[\exp \left(-\int_t^T r\left(s, X_s\right) d s\right) g\left(X_T\right) \mid \mathcal{F}_t\right] \equiv u\left(t, X_t\right)$$

## 有限元方法代写

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。

## 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|期权定价理论代写Option Pricing Theory代考|MATH4380

statistics-lab™ 为您的留学生涯保驾护航 在代写期权定价理论Option Pricing 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。我们的专家在代写期权定价理论Option Pricing Theory代写方面经验极为丰富，各种代写期权定价理论Option Pricing 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|期权定价理论代写Option Pricing Theory代考|Arbitrage and arbitrage-free models

Let us now introduce the notion of arbitrage. An arbitrage is a self-financing strategy that is worth zero initially and yields a positive gain without any risk.

DFFINITION $1.2$ Arhitrage A self-financing admissihle portfolio is called an arbitrage if the corresponding value process $\pi_t$ satisfies $\pi_0=0$ and
$$\pi_T \geq 0 \quad \mathbb{P}^{\text {hist }}-\text { a.s } \quad \text { and } \quad \mathbb{P}^{\text {hist }}\left(\pi_T>0\right)>0$$
Arbitrageurs are a special kind of trader. Their role is precisely to detect and take full advantage of arbitrage opportunities as soon as they appear in the market. This impacts market prices: arbitrage opportunities tend to disappear as soon as they arise. Absence of arbitrage opportunities is therefore a natural modeling assumption. The next lemma gives a sufficient condition under which we exclude arbitrage opportunities in our market model.
LEMMA 1.1 Sufficient condition excluding arbitrage
Suppose there exists a measure $\mathbb{Q}$ on $\left(\Omega, \mathcal{F}T\right)$ such that ${ }^3 \mathbb{Q} \sim \mathbb{P}^{\text {hist }}$ and such that, for all asset $X^i$, the discounted price process $\left{\tilde{X}_t^i\right}{t \in[0, T]}$ is a local mar-tingale with respect to $\mathbb{Q}^4{ }^4$ Then the market $\left{X_t\right}_{t \in[0, T]}$ has no arbitrage.
Note that the assumption of Lemma $1.1$ bears only on assets $X^i$ only, not on non-tradable components of $X$, such as instantaneous interest rates, instantaneous stochastic volatility, etc.

## 金融代写|期权定价理论代写Option Pricing Theory代考|Super-replication

Let us assume that, at time $t$, we buy and delta-hedge a European option ${ }^5$ written on $m$ assets, say $X_t^1, \ldots, X_t^m$, with maturity $T$ and payoff $F_T$, at the price $z$. In general, the payoff $F_T$ is a function of the paths $\left(X_t^i, 0 \leq t \leq T\right)$ followed by the prices of the $m$ assets between times 0 and $T$. The final value of the buyer’s portfolio, discounted at time 0 , is
\begin{aligned} \tilde{\pi}T^B & =-D{0 t} z+\sum_{i=1}^m \int_t^T \Delta_s^i d \tilde{X}s^i+D{0 T} F_T \ & =-D_{0 t} z+\int_t^T \Delta_s \cdot d \tilde{X}s+D{0 T} F_T \end{aligned}
Wè can then define the buyer’s super-réplication price at time $t$ as the greatest price $z$ such that the value of the buyer’s portfolio $\tilde{\pi}T^B$ is $\mathbb{P}^{\text {hist }}$-a.S. nonnegative. To be precise, we introduce the following: DEFINITION $1.4$ Buyer’s price $\mathcal{B}_t\left(F_T\right)=\sup \left{z \in \mathcal{F}_t \mid\right.$ there exists an admissible portfolio $\Delta$ such that $$\left.\tilde{\pi}_T^B \equiv-D{0 t} z+\int_t^T \Delta_s \cdot d \tilde{X}s+D{0 T} F_T \geq 0 \mathbb{P}^{\text {hist }}-a . s .\right}$$
The price $z$ must be $\mathcal{F}t$-measurable, denoted by $z \in \mathcal{F}_t$, i.e., we cannot look into the future. Similarly, we can define the seller’s super-replication price as: DEFINITION $1.5$ Seller’s price $\mathcal{S}_t\left(F_T\right)=\inf \left{z \in \mathcal{F}_t \mid\right.$ there exists an admissible portfolio $\Delta$ such that $$\left.\tilde{\pi}_T^S \equiv D{0 t} z+\int_t^T \Delta_s \cdot d \tilde{X}s-D{0 T} F_T \geq 0 \mathbb{P}^{\text {hist }}-a . s .\right}$$

# 期权理论代写

## 金融代写|期权定价理论代写Option Pricing Theory代考|Arbitrage and arbitrage-free models

$$\pi_T \geq 0 \quad \mathbb{P}^{\text {hist }}-\text { a.s } \text { and } \mathbb{P}^{\text {hist }}\left(\pi_T>0\right)>0$$

## 金融代写|期权定价理论代写Option Pricing Theory代考|Super-replication

$$\tilde{\pi} T^B=-D 0 t z+\sum_{i=1}^m \int_t^T \Delta_s^i d \tilde{X} s^i+D 0 T F_T \quad=-D_{0 t} z+\int_t^T \Delta_s \cdot d \tilde{X} s+D 0 T F_T$$

, i. e., wecannotlookintothe future. Similarly, wecandefinetheseller’ssuper – replicatio
thereexistsanadmissibleportfolio $\$ 三角洲suchthat

## 有限元方法代写

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。

## 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|期权定价理论代写Option Pricing Theory代考|MATH424

statistics-lab™ 为您的留学生涯保驾护航 在代写期权定价理论Option Pricing 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。我们的专家在代写期权定价理论Option Pricing Theory代写方面经验极为丰富，各种代写期权定价理论Option Pricing 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|期权定价理论代写Option Pricing Theory代考|Models of financial markets

Let us consider a filtered probability space $\left(\Omega,\left(\mathcal{F}t\right){0 \leq t \leq T}, \mathbb{P}^{\text {hist }}\right)$. Here $\mathbb{P}^{\text {list }}$ is the historical or real probability measure under which we model our market. A market model is defined by an $n$-dimensional stochastic differential equation $(\mathrm{SDE})$
$$d X_t^i=b_i\left(t, X_t\right) d t+\sum_{j=1}^d \sigma_{i, j}\left(t, X_t\right) d W_t^j, \quad i \in{1, \ldots, n}$$
and by another positive stochastic process $B_t$, called the money-market account, representing the value of cash, which satisfies
$$d B_t=r_t B_t d t, \quad B_0=1$$
i.e.,
$$B_t=\exp \left(\int_0^t r_s d s\right)$$

$r_t$ is the short term interest rate. It is adapted to $\mathcal{F}t$, which is the (natural) filtration generated by the $d$-dimensional uncorrelated standard Brownian motion $\left{W_t^j\right}{1 \leq j \leq d}$. In order to ensure that SDE (1.1) admits a unique strong solution (see e.g., [13]), we assume that $b$ and $\sigma$ satisfy:

Assum(SDE): The functions $b$ and $\sigma$ are Lipschitz-continuous in $x$ uniformly in $t$, and satisfy a linear growth condition: there exists a positive constant $C$ such that for all $t \geq 0, x, y \in \mathbb{R}^n$,
\begin{aligned} |b(t, x)-b(t, y)|+|\sigma(t, x)-\sigma(t, y)| & \leq C|x-y| \ |b(t, x)|+|\sigma(t, x)| & \leq C(1+|x|) \end{aligned}
We set
$$D_{t u} \equiv B_t B_u^{-1}=\exp \left(-\int_t^u r_s d s\right)$$
which is the discount factor from date $u$ to date $t$. Throughout the book, we will denote by $\tilde{Y}t \equiv D{0 t} Y_t$ the discounted value of any price process $Y_t$. Certain market components $X^i$ may not be sold or bought in the market, such as the short term interest rate, or a stochastic volatility. Throughout this book, a market component $X^i$ that can be sold and bought in the market is called an “asset.”

## 金融代写|期权定价理论代写Option Pricing Theory代考|Self-financing portfolios

Let us assume that we have a portfolio consisting of $m$ assets, say $X_t^1, \ldots, X_t^m$, and the money-market account $B_t$. It is convenient to use the notation $X^0$ for $B$. The portfolio at a time $t$ is composed of $\Delta_t^i$ assets $X_t^i$ and $\Delta_t^0$ units of $X_t^0$ (cash). The $\Delta_t^i$ ‘s must be $\mathcal{F}t$-measurable, i.e., we cannot look into the future. The portfolio value $\pi_t$ is $$\pi_t \equiv \sum{i=0}^m \Delta_t^i X_t^i$$
As time passees, wẽ can readjust the allocations $\Delta_t^i$, but no cash is éver injéctéd or removed from the portfolio: between $t$ and $t+d t$, the variation in the portfolio value is only due to the variation of the values of the assets, i.e.,
$$d \pi_t=\sum_{i=0}^m \Delta_t^i d X_t^i$$
We then speak of a self-financing portfolio. In terms of discounted values, this rears
$$d \tilde{\pi}t=\sum{i=0}^m \Delta_t^i d \tilde{X}t^i=\sum{i=1}^m \Delta_t^i d \tilde{X}_t^i$$

because for any price process $Y_t, d \tilde{Y}t=D{0 t}\left(d Y_t-r_t Y_t d t\right)$, concluding that ${ }^2$
$$\tilde{\pi}t=\pi_0+\sum{i=1}^m \int_0^t \Delta_s^i d \tilde{X}_s^i$$
We may also write this as
$$\tilde{\pi}_t=\pi_0+\int_0^t \Delta_s \cdot d \tilde{X}_s$$
where $\cdot$ denotes the usual scalar product in $\mathbb{R}^m$. As a technical condition, we need to introduce the notion of admissible portfolio:

DEFINITION 1.1 Admissible portfolio $\left(\Delta_t, 0 \leq t \leq T\right)$ defines an admissible portfolio if $\tilde{\pi}_t$ is bounded from below for all $t \mathbb{P}^{\text {hist }}$-a.s., i.e., there exists $M \in \mathbb{R}$ such that
$$\mathbb{P}^{\text {hist }}\left(\forall t \in[0, T], \tilde{\pi}_t \geq M\right)=1$$

# 期权理论代写

## 金融代写|期权定价理论代写Option Pricing Theory代考|Models of financial markets

$$d X_t^i=b_i\left(t, X_t\right) d t+\sum_{j=1}^d \sigma_{i, j}\left(t, X_t\right) d W_t^j, \quad i \in 1, \ldots, n$$

$$d B_t=r_t B_t d t, \quad B_0=1$$
$\mathrm{IE}{\mathrm{O}}$ $$B_t=\exp \left(\int_0^t r_s d s\right)$$ $\mathrm{SDE}(1.1)$ 承认唯一的强解决方案（参见例如 [13]），我们假设 $b$ 和 $\sigma$ 满足: 假设 (SDE) : 函数 $b$ 和 $\sigma$ 是 Lipschitz 连续的 $x$ 统一在 $t$ ，并且满足线性增长条件：存在正常数 $C$ 这样对于 所有人 $t \geq 0, x, y \in \mathbb{R}^n$ ， $$|b(t, x)-b(t, y)|+|\sigma(t, x)-\sigma(t, y)| \leq C|x-y||b(t, x)|+|\sigma(t, x)| \quad \leq C(1+|x|)$$ 我们设置 $$D{t u} \equiv B_t B_u^{-1}=\exp \left(-\int_t^u r_s d s\right)$$

## 金融代写|期权定价理论代写Option Pricing Theory代考|Self-financing portfolios

$$\pi_t \equiv \sum i=0^m \Delta_t^i X_t^i$$

$$d \pi_t=\sum_{i=0}^m \Delta_t^i d X_t^i$$

$$d \tilde{\pi} t=\sum i=0^m \Delta_t^i d \tilde{X} t^i=\sum i=1^m \Delta_t^i d \tilde{X}_t^i$$

$$\tilde{\pi} t=\pi_0+\sum i=1^m \int_0^t \Delta_s^i d \tilde{X}_s^i$$

$$\tilde{\pi}_t=\pi_0+\int_0^t \Delta_s \cdot d \tilde{X}_s$$

$$\mathbb{P}^{\text {hist }}\left(\forall t \in[0, T], \tilde{\pi}_t \geq M\right)=1$$

## 有限元方法代写

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。

## 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。

• 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

Power generation costs consist of fixed costs (e.g., equipment depreciation costs, labor costs, and maintenance costs) and variable costs (e.g., fuel costs and exhaust processing costs). However, the variable cost is almost the fuel cost. Therefore, the unit cost of gas-fired generation expressed as Cost and the corresponding natural gas procurement cost expressed as Gas satisfy the following equation:
$$\text { Cost }=\alpha_0 \times \text { Gas }+\alpha_1,$$
where $\alpha_0$ and $\alpha_1$ are the coefficients. Although the Henry Hub futures price HenryHub $b_{f, t}$ and the PJM futures price $P J M_{f, t}$, both of which are unit root processes, are cointegrated, the long-term equilibrium equation, which is a stochastic process, can have large outliers. Then, when the own spot spread, that is, the difference between the power generation unit cost and the corresponding gas procurement unit price, is smaller than the future spread, that is, the future price difference between the PJM and Henry Hub, we swap the spot spread, Spread $_s$ and the future spread, Spread $_f$, which we express as
$$\begin{gathered} \text { Spread }s=\alpha_0 \times \text { HenryHub }{f, t}+\alpha_1-\text { HenryHub }{f, t} \ \text { Spread }_f=\text { PJM }{f, t}-\text { HenryHub } b_{f, t} . \end{gathered}$$
Therefore, the difference between these spreads is
$$\text { Spread }s-\text { Spread }_f=\alpha_0 \times \text { HenryHub } b{f, t}+\alpha_1-P J M_{f, t} .$$
In the following equation:
$$\alpha_0 \times \text { HenryHub } b_{f, t}+\alpha_1-P J M_{f, t}<0 .$$
If we take the Henry Hub long position and the PJM short position corresponding to the electric energy planned for generation, we can lock in profit.

By estimating the long-term equilibrium equation of $H e n r y H u b_{f, t}$ and $P J M_{f, t}$ in a cointegration relationship, we can determine whether the futures spread on a candidate trading date is wider or narrower than the expected spread. This determination enables statistical arbitrage trading between Henry Hub and PJM.

Since the prices in period $t$ are not available for trading in period $t$, we estimate the following long-term equilibrium equation using the price series up to period $t-1$ :
$$P J M_{f, t}=\beta_{f, 0} \times \text { HenryHub} b_{f, t}+\beta_{f, 1^{\circ}} .$$
If the futures spread is higher than the expected value, then we express it as
$$P J M_{f, t}>\beta_{f, 0} \times \text { HenryHub } b_{f, t}+\beta_{f, 1} .$$
We can consider that the PJM price is higher and the Henry Hub price is lower; therefore, we take the PJM short position and Henry Hub long position. Then, the condition for closing these arbitrage positions is
\begin{aligned} \text { PJM }{f, t} &-\text { avgShort } P J M_f+\operatorname{avg} \text { LongHenryHub } \ &-\text { HenryHub } b{f, t}>0 \end{aligned}
where avg Short $P J M_f$ is the average price of the PJM futures short positions taken, and avgLong Henry Hub $b_f$ is the average price of the Henry Hub futures long positions taken. The clearance of all these futures positions under this condition leads to profit.

Conversely, if the futures spread is below the expected value, then we express it as
$$P J M_{f, t}<\beta_{f, 0} \times \text { HenryHub} b_{f, t}+\beta_{f, 1} .$$
We determine that the PJM price is lower and the Henry Hub price is higher; therefore, we take the PJM long position and Henry Hub short position.

# 交易策略代考

$$\text { Cost }=\alpha_0 \times \text { Gas }+\alpha_1,$$

Spread $s=\alpha_0 \times$ HenryHub $f, t+\alpha_1-$ HenryHub $f, t$ Spread $_f=\operatorname{PJM} f, t-$ HenryHub $b_{f, t}$.

Spread $s-$ Spread $_f=\alpha_0 \times$ HenryHub $b f, t+\alpha_1-P J M_{f, t}$.

$$\alpha_0 \times \text { HenryHub } b_{f, t}+\alpha_1-P J M_{f, t}<0 .$$

$$P J M_{f, t}=\beta_{f, 0} \times \text { HenryHub } b_{f, t}+\beta_{f, 1^{\circ}} .$$

$$P J M_{f, t}>\beta_{f, 0} \times \text { HenryHub } b_{f, t}+\beta_{f, 1} .$$

$$\text { PJM } f, t-\operatorname{avgShort} P J M_f+\text { avg LongHenryHub } \quad-\text { HenryHub } b f, t>0$$

$$P J M_{f, t}<\beta_{f, 0} \times \text { HenryHubb } b_{f, t}+\beta_{f, 1} .$$

## 有限元方法代写

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。

## 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。

• 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

We can estimate the cointegrating vectors by using dynamic OLS (DOLS). OLS estimates the following equation with lag terms for the explanatory variables to climinate autocorrelation:
$$x_{v, t}=\varphi_0+\sum_{i=1}^{v-1}\left(\beta_i \varphi_{i, t}+\sum_{j=-K}^K \phi_{i, j} \Delta x_{i, t-j}\right) .$$
Since Sect. 2.2.4 utilizes a two-variable model, the model for estimating the longterm equilibrium is
$$P J M_t=\varphi_0+\varphi_1 \text { HenryHub}t+\sum{j=-K}^K \phi_j \Delta \text { Henry Hub } b_{t-j} .$$
The lag order $K$ was determined using SBIC. The long-term equilibrium equation for future prices is
The long-term equilibrium equation for the spot prices is
$$P J M_{\text {spot }}=11.142 \times \text { HenryHub }_{\text {spot }}+5.732 .$$

The only way to profit by trading goods is to “buy at a lower price and sell at a higher price.” If we trade only one item, then price forecasting is the most important matter. Is this realistically possible? A market is efficient if the information that affects the market price is comprehensive, constant, and has a timely effect on the price. Markets for securities and commodities listed on exchanges are almost efficient and depend on liquidity. In other words, we cannot forecast the price because the price already reflects all the currently available information, and any information that affects the price will occur independently of the price. Unfortunately, it is impossible for market participants to earn returns above the market average. Certainly, a “fully efficient market” is theoretical or virtual. Therefore, some investors and speculators try to collect information before it is reflected in the price. However, these actions make the market more efficient. Because the stationary hypothesis for most energy prices is rejected by the unit root test using daily data, energy companies should consider energy markets as efficient, and energy prices as unpredictable.

In general, power companies procure various types of fuels from various markets, produce electricity using various power generation methods, and sell the power through various sales channels. Section $2.3$ assumes a simple model of purchasing natural gas at the Henry Hub price and selling electricity at the PJM price, as Fig. $2.8$ illustrates. We propose two trading strategies. Section $2.4$ will simulate these methods using actual historical data. Both focus not on these prices but on the price difference between Henry Hub prices and PJM prices. We cannot expect profit owing to market efficiency, even if we analyze each price in detail. On the other hand, we demonstrate the potential to make a profit by investigating price differences, which is a stationary process. When buying the gas required to produce one unit of electricity and selling it, the gross margin is often called the spark spread.

The trading strategy introduced in Sect. 2.3.1 is the arbitrage between the futures market spreads and a company’s spreads expected from its power generation efficiency. This takes advantage of the spread of futures as a stochastic process. All we have to do is take the Henry Hub long position and the PJM short position to secure profits when a favorable futures spread occurs stochastically. The strategy proposed in Sect. 2.3.2 is statistical arbitrage utilizing the cointegration relationship between Henry Hub prices and PJM prices in the futures market. Making use of the longterm equilibrium equation in the futures market that expresses the futures spread, the lower PJM long positions and the higher Henry Hub short positions are expected to yield profit in the narrower spreads than the market when the spread approaches the long-term equilibrium.

# 交易策略代考

$$x_{v, t}=\varphi_0+\sum_{i=1}^{v-1}\left(\beta_i \varphi_{i, t}+\sum_{j=-K}^K \phi_{i, j} \Delta x_{i, t-j}\right) .$$

$$P J M_t=\varphi_0+\varphi_1 \text { HenryHubt }+\sum j=-K^K \phi_j \Delta \text { Henry Hub } b_{t-j} .$$

$$P J M_{\text {spot }}=11.142 \times \text { HenryHub }_{\text {spot }}+5.732 .$$

Sect. 中介绍的交易策略。2.3.1 是期货市场价差与公司发电效率预期价差之间的套利。这利用了期货的价差作为一个随机过程。我们所要做的就是在随机出现有利的期货价差时，持有 Henry Hub 多头头寸和 PJM 空头头寸以确保获利。节中提出的策略。2.3.2是统计套利，利用期货市场上Henry Hub价格和PJM价格之间的协整关系。利用期货市场中表示期货价差的长期均衡方程，当价差接近长期均衡时，较低的 PJM 多头头寸和较高的 Henry Hub 空头头寸有望在比市场更窄的价差中产生利润.

## 有限元方法代写

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。

## 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。

• 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

Before conducting various analyses and simulations, it is extremely important to interpret the representative statistics of the data. Table $2.1$ provides the summary statistics of the Henry Hub and the PJM.

Considering that each future has a maturity of one month, we set each spot price to January 29, 2021 and each future to December 30, 2020 to simulate the spot-future arbitrage described later in Sect. 2.3.1. Because we extract only the days when both the Henry Hub and PJM data are available, we have 1511 and 1477 observations for the futures and spot prices, respectively.

The mean and median are numerical values located in the center of the economic variables. The mean $\bar{x}$ of the series $\left(x_i \mid i=1,2, \ldots, N\right)$ is calculated as
$$\bar{x}=\frac{1}{N} \sum_{i=1}^N x_i .$$
On the other hand, the median is a value located in the center of each series arranged in descending order. The medians of these futures and spot series are at the 756th and 739th values, respectively. If the number of observations is even, then the median is the average of the two data points in the center. Thus, the median is a more stable index expressing the middle than the mean because outlier values have less effect. Figure $2.1$ shows three distribution examples with the same mean, but different medians. Table $2.1$ indicates that both the mean and median of each future are higher than those of each spot. In other words, both Henry Hub and PJM tend to be contango. We can infer that the supply and demand are not very tight during this period. We can express the relationship between the future price $p_f$ and its spot price $p_s$ as
$$p_f=p_s e^{c_c \Delta T},$$ where $C_c$ is the cost of carry expressed in terms of yield and $\Delta T$ is the period from the present to maturity. The cost of carry is the sum of the risk-free interest rate and holding cost, expressed as yield minus the convenience yield. Therefore, if their supply and demand remained tight during the period, then the utility of holding their spots would be increasing. Thus, their costs of carry should become negative, and their futures should become lower than their spots. In addition, the medians of both the Henry Hub future and spot prices are higher than their respective means. Therefore, we can expect to find many outliers in the left tail of each distribution. On the contrary, the medians of both the PJM future and spot prices are lower than their respective means. Therefore, we can expect to find many outliers in the right tail of each distribution.

Figures $2.1$ and $2.2$ bring to mind the long-term equilibrium relationship between Henry Hub and the PJM in both futures and spot markets. However, as all four variables accept the unit root hypothesis, we must suspect a spurious regression.
Engle and Granger [7] introduced the concept of “cointegration,” which connects multiple unpredictable stochastic variables with a unit root. If a linear combination of multiple unit root processes is stationary, then these variables have a cointegrated relationship. In other words: suppose that the following vector consists of $v$ variables in a unit root process:
$$\mathbf{X}t={ }^T\left(x{1 t}, x_{2 t}, \ldots, x_{v t}\right)$$
The following linear combination is derived from the inner product of the $v$ dimensional coefficient vector and $\mathbf{X}t$ : $$\boldsymbol{\beta} \mathbf{X}_t=\left(\beta_1, \beta_2, \ldots, \beta_v\right)^T\left(x{1 t}, x_{2 t}, \ldots, x_{v t}\right)$$
If $\boldsymbol{\beta} \boldsymbol{X}t$ is a stationary process, then $x{1 t}, x_{2 t}, \ldots, x_{v t}$ have a cointegrated relationship. Additionally,
$$\boldsymbol{\beta}=\left(\beta_1, \beta_2, \ldots, \beta_v\right)$$
is the cointegrating vector. If there is cointegration between some variables, then the deviation of the observed values from their long-term equilibrium is a stable stochastic process. Because many economic variables have unit roots, this concept is very often applied in a wide range of fields to examine the relationships between economic variables.

Therefore, we test whether the Henry Hub and PJM prices are cointegrated and expect to use this cointegrated relationship in the trading strategies.

Engle and Granger’s [7] proposed test for cointegration has limitations. First, it does not expect a system with three or more variables to have two or more cointegration relationships. Second, the test results may change when the variables are interchanged.

# 交易策略代考

$$\bar{x}=\frac{1}{N} \sum_{i=1}^N x_i$$

$$p_f=p_s e^{c_c \Delta T},$$

Engle 和 Granger [7] 引入了”协整”的概念，它将多个不可预测的随机变量与一个单位根联系起来。如果多个单 位根过程的线性组合是平稳的，则这些变量具有协整关系。换句话说：假设以下向量由 $v$ 单位根过程中的变量:
$$\mathbf{X} t={ }^T\left(x 1 t, x_{2 t}, \ldots, x_{v t}\right)$$

$$\boldsymbol{\beta} \mathbf{X}t=\left(\beta_1, \beta_2, \ldots, \beta_v\right)^T\left(x 1 t, x{2 t}, \ldots, x_{v t}\right)$$

$$\boldsymbol{\beta}=\left(\beta_1, \beta_2, \ldots, \beta_v\right)$$

## 有限元方法代写

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。

## 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|投资组合代写Investment Portfolio代考|FINC3017

statistics-lab™ 为您的留学生涯保驾护航 在代写投资组合Investment Portfolio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。我们的专家在代写投资组合Investment Portfolio方面经验极为丰富，各种代写投资组合Investment Portfolio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|投资组合代写Investment Portfolio代考|CLASSICAL FRAMEWORK FOR MEAN-VARIANCE OPTIMIZATION

In this section we place the intuitive discussion thus far into a more formal mathematical context and develop the theory of mean-variance optimization. Suppose first that an investor has to choose a portfolio comprised of $N$ risky assets. ${ }^8$ The investor’s choice is embodied in an $N$ vector $\mathbf{w}=\left(w_1, w_2, \ldots, w_N\right)^{\prime}$ of weights, where each weight $i$ represents the percentage of the $i$ th asset held in the portfolio, and
$$\sum_{i=1}^N w_i=1$$
For now, we permit short selling, which means that weights can be negative. Later on in this chapter we will discuss no short-selling and in Chapter 4 we consider more general constraints.

Suppose the assets’ returns $\mathbf{R}=\left(R_1, R_2, \ldots, R_N\right)^{\prime}$ have expected returns $\boldsymbol{\mu}=\left(\mu_1, \mu_2, \ldots, \mu_N\right)^{\prime}$ and an $N \times N$ covariance matrix given by
$$\boldsymbol{\Sigma}=\left[\begin{array}{ccc} \sigma_{11} & \cdots & \sigma_{1 N} \ \vdots & & \vdots \ \sigma_{N 1} & \cdots & \sigma_{N N} \end{array}\right]$$
where $\sigma_{i j}$ denotes the covariance between asset $i$ and asset $j$ such that $\sigma_{i i}=\sigma_i^2$, $\sigma_{i j}=\rho_{i j} \sigma_i \sigma_j$ and $\rho_{i j}$ is the correlation between asset $i$ and asset $j$. Under these assumptions, the return of a portfolio with weights $\mathbf{w}=\left(w_1, w_2, \ldots, w_N\right)^{\prime}$ is a random variable $R_p=w^{\prime} R$ with expected return and variance given by ${ }^9$
$$\begin{gathered} \mu_p=\mathbf{w}^{\prime} \boldsymbol{\mu} \ \sigma_p^2=\mathbf{w}^{\prime} \mathbf{\Sigma}_{\mathbf{W}} \end{gathered}$$
For instance, if there are only two assets with weights $\mathbf{w}=\left(w_1, w_2\right)^{\prime}$, then the portfolio’s expected return is
$$\mu_p=w_1 \mu_1+w_2 \mu_2$$

## 金融代写|投资组合代写Investment Portfolio代考|Increasing the Asset Universe

From theory we know that by introducing more (low-correlating) assets, for a targeted expected portfolio return, we should be able to decrease the standard deviation of the portfolio. In Exhibit $2.6$ the assumed expected returns, standard deviations, and correlations of 18 countries in the MSCI World Index are presented.

Exhibit $2.7$ illustrates how the efficient frontier widens as we go from 4 to 12 assets and then to 18 assets. By increasing the number of investment opportunities we increase our level of possible diversification.

We now ask whether it is possible in general to decrease portfolio risk (and keeping the expected portfolio return constant) by increasing the asset universe. To answer this question, we first observe that the portfolio variance can be bounded by
\begin{aligned} \operatorname{var}\left(R_p\right) &=\mathbf{w}^{\prime} \boldsymbol{\Sigma} \mathbf{w} \ &=\frac{1}{N^2} \sum_{i=1}^N \operatorname{var}\left(R_i\right)+\frac{1}{N^2} \sum_{i \neq j} \operatorname{cov}\left(R_i, R_j\right) \ & \leq \frac{1}{N^2} N \sigma_{\max }^2+\frac{1}{N^2}(N-1) N \cdot A \ &=\frac{\sigma_{\max }^2}{N}+\frac{N-1}{N} \cdot A \end{aligned}

where $\sigma_{\max }^2$ is the largest variance of all individual assets and $A$ is the average pairwise asset covariance,
$$A=\frac{1}{(N-1) N} \sum_{i \neq j} \operatorname{cov}\left(R_i, R_j\right)$$
If the average pairwise covariance $A$ and all variances are bounded, then we conclude that
$$\operatorname{var}\left(R_p\right) \underset{N \rightarrow \infty}{\longrightarrow} A$$
This implies that the portfolio variance approaches A as the number of assets becomes largé. Therefore we see that, in general, the benefits of diversification are limited up to a point and that we cannot expect to be able to completely eliminate portfolio risk.

# 投资组合代考

## 金融代写|投资组合代写Investment Portfolio代考|CLASSICAL FRAMEWORK FOR MEAN-VARIANCE OPTIMIZATION

$$\sum_{i=1}^N w_i=1$$

$$\mu_p=\mathbf{w}^{\prime} \boldsymbol{\mu} \sigma_p^2=\mathbf{w}^{\prime} \boldsymbol{\Sigma}_{\mathbf{W}}$$

$$\mu_p=w_1 \mu_1+w_2 \mu_2$$

## 金融代写|投资组合代写Investment Portfolio代考|Increasing the Asset Universe

$$\operatorname{var}\left(R_p\right)=\mathbf{w}^{\prime} \boldsymbol{\Sigma} \mathbf{w} \quad=\frac{1}{N^2} \sum_{i=1}^N \operatorname{var}\left(R_i\right)+\frac{1}{N^2} \sum_{i \neq j} \operatorname{cov}\left(R_i, R_j\right) \leq \frac{1}{N^2} N \sigma_{\max }^2+\frac{1}{N^2}(N-1) N$$

$$A=\frac{1}{(N-1) N} \sum_{i \neq j} \operatorname{cov}\left(R_i, R_j\right)$$

$$\operatorname{var}\left(R_p\right) \underset{N \rightarrow \infty}{\longrightarrow} A$$

## 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。