### 金融代写|金融计量经济学代写Financial Econometrics代考|Steps to Be Followed in Applied

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• 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|金融计量经济学Financial Econometrics代考|Problem Definition or Statement

Problem definition or statement is the very first step for conducting any financial econometrics study. It is very important to identify the clear-cut research problem or what study is going to address? For an instant let’s define the study statement as “To examine the performance of Capital Asset Pricing Model (CAPM) in Indian context”. Based on the study problem following hypotheses are framed:
$\begin{array}{ll}\text { Null Hypothesis }(H o): & \text { CAPM regression intercept is cqual to } \ \text { Alternative Hypothesis (Ha): } & \text { CAPM. regression intercept is not equal } \ & \text { to zero. }\end{array}$
As the entire study centres around the framed hypotheses and a minor mistake in framing the hypotheses could result into an absolute disaster. So, any financial econometrics study requires in-depth understanding of the hypotheses testing for the different statistical methods and econometrics models used. Testing of hypothesis concludes either with rejection of the null hypothesis or acceptance of the alternative hypothesis. Rejection of the null hypothesis indicates that the null hypothesis is not true and alternative hypothesis could be accepted or vice versa. Decision of acceptance or rejection of the null hypothesis is based on the obtained $p$-values or $t$-values.

Wrongly rejecting the null or alternative hypothesis could give rise to the circumstances of Type I (alpha) and Type II (beta) errors. Rejection of null hypothesis even when that’s true would result into Type I error. Likewise fail to reject the null hypothesis even when that’s false would result into Type II error. Type I errors largely arise due to scepticisms and by selecting the correct critical values it could be climinated to a greater degree. In practice depending on the type of datasets different critical values that are being used are based on the 1,5 or $10 \%$ statistical significance level. In contrast Type II errors could be prevented mostly using large sample size. Second approach might be the choosing higher level of significance. The impact of Type I and Type II errors ruling can be accessed through determining the appropriate level of statistical significance. This is how the appropriate level of statistical significance is powerful. Next crucial question is “Which type of the errors are the worst: Type I or II”? Well, both Type I and II errors could be worst depending upon the context. By and large Type I errors are more serious than Type II errors.

## 金融代写|金融计量经济学Financial Econometrics代考|Selection of Variables

Based on the study statement wisely selects the study variables. Selection of variables is the another important task in financial econometrics study. Most of the financial econometrics study deals with the closest proxy variables available as it is very difficult to obtained the real variables data. For example, it is very difficult to obtain the real estimates for the market returns. Influential Stock Index benchmarks (S\&P 500; BSE 30; NSE 50, and others) are often used as the proxy for market returns. For the study statement “To examine the performance of Capital Asset Pricing Model (CAPM) in Indian context”, study variables could be as follows: individual stocks or portfolio excess returns (dependent variable); risk free rate (91 days T-bills); market returns (BSE 30 or BSE-200 index monthly excess returns). For financial econometrics studies, data could be obtained from both paid and unpaid databases or data sources. Bloomberg, Thomson Reuters Eikon, Capitaline, Compustat, Datastream, CMIE Prowess, and others are some of the leading providers of financial data or databases based on subscriptions. Likewise Yahoo finance, central banks, World bank, IMF, Stock exchanges, Google finance, SEC, CoinMarketCap (Cryptocurrency), and others are some of the leading providers of public or free financial data or databases. Once variables are chosen next study period and frequency of data (daily, weekly, monthly, yearly or others) has to be specified.

## 金融代写|金融计量经济学Financial Econometrics代考|Model Description

Once the study variables are identified based on it develop the financial econometric model. Mathematically represent the developed financial econometric model taking into account all possible factors or variables that could affect the dependent variable(s). For example, the CAPM illustrates the relationship between market (systematic risk) and expected return for assets. Here the relationship between market (systematic risk) and expected return for assets is not exact and deterministic, rather a typical or stochastic one. Morcover in financial econometrics study the relationship between the variables often has two way causality. These types of situations demand for a stochastic specification in the model. And that is possible by inserting “stochastic terms” or “Error terms” or “noise terms” or “disturbance terms” or “residuals terms” in the model. Other justification for inclusion of the stochastic specification $(\varepsilon)$ in the model could take into account the crratic human behaviour, influence of omitted variables, measurement errors if any and others. To examine the performance of CAPM in Indian context following model is developed as shown below in Eq. 1.1:
$$R_{\mathrm{pit}}-R_{\mathrm{ft}}=\alpha+\beta\left(R_{\mathrm{mt}}-R_{\mathrm{ft}}\right)+\varepsilon_{\mathrm{it}}$$
where “residuals terms”.
Stochastic specification $(\varepsilon)$ in the above model is not readily observable like the other variables. Often study in applied financial econometrics makes some reasonable assumptions about the shape of the distribution of $\operatorname{cach}(\varepsilon)$.
Which are as follows:

• Error terms $(\varepsilon)$ are normally distributed
• Mean of the error terms ( $\varepsilon$ ) is zero
• Error terms $(\varepsilon)$ have uniform variance $\left(\sigma^{2}\right)$ or homoscedastic
• Error terms $(\varepsilon)$ are independent or uncorrelated to each other.

## 金融代写|金融计量经济学Financial Econometrics代考|Problem Definition or Statement

零假设 (H○): CAPM 回归截距等于   替代假设（Ha）：  CAPM。回归截距不相等   为零。

## 金融代写|金融计量经济学Financial Econometrics代考|Model Description

Rp一世吨−RF吨=一个+b(R米吨−RF吨)+e一世吨

• 错误术语(e)是正态分布的
• 误差项的平均值 (e) 为零
• 错误术语(e)有一致的方差(σ2)或同调
• 错误术语(e)彼此独立或不相关。

## 有限元方法代写

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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。