### 统计代写|r语言作业代写代做|Portfolio Theory

R是一种用于统计计算和图形的编程语言，由R核心团队和R统计计算基金会支持。R由统计学家Ross Ihaka和Robert Gentleman创建，在数据挖掘者和统计学家中被用于数据分析和开发统计软件。用户已经创建了软件包来增强R语言的功能。

statistics-lab™ 为您的留学生涯保驾护航 在代写r语言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r语言代写方面经验极为丰富，各种代写r语言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

## 统计代写|r语言作业代写代考|Portfolio Theory

In this section we devote three chapters to the relationship between risk and return. These topics are the most theoretical that we have covered yet, but we will not be delving into the theory. Instead, we will focus on code flows.

First, we will discuss the Sharpe Ratio, a measure of the return versus risk ratio of a portfolio.

Then, we will look at the Capital Asset Pricing Model (CAPM) and specifically how to calculate the market beta for our assets and portfolio. This will be an introduction to simple linear regression.

We will conclude with an exploration of the Fama-French multi-factor model, which also serves as an introduction to multiple linear regression.

If you wish to study further into these topics, see Sharpe’s 1964 article, “Asset Prices: A Theory of Market Equilibrium under Conditions of Risk”, ${ }^{1}$ Sharpe’s 1994 article “The Sharpe Ratio”, ${ }^{2}$ and “Common risk factors in the returns on stocks and bonds” $” 3$ by Fama and French.

From a general data science perspective, we have covered data import and wrangling in the first section, descriptive statistics in the second section, and this section is devoted to the modeling and evaluating of our data.
We will accomplish the following in this section:
1) calculate and visualize the Sharpe Ratio and the rolling Sharpe Ratio
2) calculate and visualize CAPM beta
3) calculate and visualize the Fama-French 3-Factor Model and the rolling Fama-French 3-Factor model
4) build Shiny apps for Sharpe Ratio, CAPM beta and rolling FamaFrench model
We will be working with the portfolio returns objects that were created in the Returns section. If you are starting a new $\mathrm{R}$ session and want to run the code to build those objects, navigate here:

## 统计代写|r语言作业代写代考|Sharpe Ratio

The Sharpe Ratio is defined as the mean of the excess monthly portfolio returns above the risk-free rate, divided by the standard deviation of the excess monthly portfolio returns above the risk-free rate. This is the formulation of the Sharpe Ratio as of 1994 ; if we wished to use the original formulation from 1966 the denominator would be the standard deviation of all the monthly portfolio returns.

The Sharpe Ratio measures excess returns per unit of risk, where we again take the standard deviation to represent portfolio risk. The Sharpe Ratio was brought to us by Bill Sharpe – arguably the most important economist for modern investment management as the creator of the Sharpe Ratio, CAPM (which we will cover later) and Financial Engines, a forerunner of today’s robo-advisor movement.
The Sharpe Ratio equation is as follows:
Sharpe Ratio $=\left(\overline{R_{p}-R_{f}}\right) / \sigma_{\text {excess }}$
The numerator is the mean excess return above the risk-free rate and the denominator is the standard deviation of those excess returns. In other words, it is the ratio of return to risk and so a higher Sharpe Ratio indicates a ‘better’ portfolio.

We will start with the built-in function from the xts world and will look at the by-hand equation as part of the tidyverse.

## 统计代写|r语言作业代写代考|Visualizing Sharpe Ratio

Before visualizing the actual Sharpe, we will get a sense for what proportion of our portfolio returns exceeded the RFR.

When we originally calculated Sharpe by-hand in the tidyverse, we used summarise to create one new cell for our end result. The code was summarise $(r a t i o=$ mean (returns $-r f r) / s d(r e t u r n s-r f r))$.

Now, we will make two additions to assist in our data visualization. We will add a column for returns that fall below the risk-free rate with mutate (returns_below_rfr $=$ ifelse(returns < $r r$, returns, NA)) and add a column for returns above the risk-free rate with mutate(returns_above_rfr = ifelse(returns > rfr, returns, NA)).
This is not necessary for calculating the Sharpe Ratio, but we will see how it illustrates a benefit of doing things by-hand with dplyr: if we want to extract or create certain data transformations, we can add it to the piped code flow.

## 统计代写|r语言作业代写代考|Portfolio Theory

1) 计算和可视化夏普比率和滚动夏普比率
2) 计算和可视化 CAPM beta
3) 计算和可视化 Fama-French 3-Factor 模型和滚动 Fama-French 3-因子模型
4) 为夏普比率、CAPM beta 和滚动 FamaFrench 模型构建闪亮的应用程序

## 广义线性模型代考

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## 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。