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Modelling Risk Coursework
Introduction
This project consists of analysing the performance of two individual stocks; namely HSBC Holdings and Easyjet. Then compare them with the overall market of the London Stock Exchange.
For these two stocks, I collected their past three years’ weekly price from https://uk.finance.yahoo.com . (See Table 1&2)
Analysis
First, I calculated the weekly return for each stock by Excel. The formula of the weekly return is namely, let r to denote weekly return, then
 
I used the adjusted closing position as  . For example, the week 1’s return of HSBC Holdings is the adjusted closing position of week 1, subtract the adjusted closing position of week 2, divide by the closing position of week 2 e.g.
 
I’m using FTSE100 index as an approximation of the overall market. I also collected the past three years’ weekly price of FTSE, this is in Table3. I used the same method to work out the weekly return of the overall market, to compare with the two individual stocks.
I now work out the mean weekly return and the standard deviation for each stock and the overall market. In the rest of the sections, I will refer HH for HSBC Holdings, EA for Easyjet, OM for the overall market for easier reference.
The average weekly return   is the sum of all weekly returns during the past three years, divide by the total number of weeks.
 
 Standard deviation (ℴ) is the positive square root of the variance. The sample variance is computed by the formula
 
These are computed with the aid of Excel, the results are shown on Table 3.
Notice that, the standard deviation of OM is substantially smaller than for the individual stocks HH and EA. It shows that OM is less risky than the other two, as it has smaller fluctuations.
We now create a portfolio, call it portfolio A, it invests 90% of the asset on OM, 10% on EJ.
Now we have another random variable, portfolio A, which depends on OM and EJ, write the equation explicitly:
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Then, the expected value of PA,  , is:
 
by the linearity of the Expectation function, we have
 
We work out the variance of PA from first principle of variance, and hence deduce the standard deviation. We get the
 
Now, we run a regression analysis of stock returns against market returns.
For HSBC Holdings:
SUMMARY      
      
Regression Statistics     
Multiple R 0.691428257     
R Square 0.478073035     
Adjusted R Square 0.474683899     
Standard Error 0.039920674     
Observations 156     
      
ANOVA      
  df SS MS F Significance F 
Regression 1 0.224802417 0.224802417 141.0604402 1.66222E-23 
Residual 154 0.245423679 0.00159366   
Total 155 0.470226096       
      
  Coefficients Standard Error t Stat P-value Lower 95% Upper 95%
Intercept 0.001846841 0.003196345 0.577797924 0.56424本论文由英语论文网提供整理，提供论文代写，英语论文代写，代写论文，代写英语论文，代写留学生论文，代写英文论文，留学生论文代写相关核心关键词搜索。