数据分析代写 | 7SSMM603 Research Methods Data Analysis Essay Guidance Notes

本次英国代写是一个数据分析+1000字报告相关的Assignment

Analysis task:

To understand if dividend yield (DY), term spread (TERM) and variance risk premium (VRP) can predict ex post S&P 500 index returns.

Methodology:

A number of econometrics models can be used to test such relationship. You will only need to choose one. One of the simplest forms will be long horizon regression as in Bollerslev, Tauchen and Zhou (2009). There is no requirement on what horizon you should include so you can use your own judgement here.

Data:

You will need monthly S&P 500 index returns, dividend yield (DY), term spread (TERM) and variance risk premium (VRP). After a simple search, you should be able to find a number of data sources. Here are examples of data sources for each variable:

S&P 500 index returns: WRDS CRSP, FRED Economic Data (https://fred.stlouisfed.org), Thomson Eikon and Bloomberg

Dividend Yield: Thomson Datastream, Bloomberg and an online source (https://www.multpl.com/s-p-500-dividend-yield)

Term spread: FRED Economic Data provides readily computed term spread which can be found here: https://fred.stlouisfed.org/series/T10Y3M.

Variance risk premium: The third author in Bollerslev, Tauchen and Zhou (2009) shares readily computed VRP figures (https://drive.google.com/open?id=0Bwai7ZTn1nJSbThUcHp2OTFoUWs)

Software:

There is no specific requirement for the software you should use. You can choose the one you are most familiar with or from the following: Eviews, Stata, R, SPSS, Matlab and Excel. Indeed, one can focus on one-month prediction horizon and simply use Excel for the task.

Expectation:

This assessment is a valuable chance for you to try handling the data and run regression(s). As you can see from the word limit of 1000 words, this is a very short report and there is no expectation that you will submit a very comprehensive report. The idea is that in the assignment you write about what you have learned from the data handling process. It does not matter if you make mistakes in your analysis, but it is important to acknowledge them and discuss what needs to be done to overcome / improve.

This way, you will know what to pay attention to in your dissertation’s data analysis.

Reference:

Bollerslev, T., Tauchen, G. and Zhou, H., 2009. Expected stock returns and variance risk premia. The Review of Financial Studies, 22(11), pp.4463-4492.


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