Python

Part 1:                        (50 points)

  1. Download monthly price data of S&P500 and a stock of your choice for the period 01/01/2004 to 12/31/2019 (or any fifteen year period)                                                        (2 points)
  2. Compute the monthly returns for the S&P 500 and the stock. Construct one data frame to store the return series. (5 points)

 

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  • Construct summary statistics, histogram, correlation matrix of the return series. (4 points)

 

  1. Download 3 month TBill rate from Fred. Consider the TBill data for the same sample period 01/01/2004 to 12/31/2019 (or any fifteen year period that you have chosen) . (8 points)

 

  1. Construct a matrix of return series combining Stock, S&P500, and TBill for the sample period. (6 points)

 

  1. Find Beta for the stock based on the sample data using CAPM model. (4 points)
  • Test the null hypothesis:         ; what do you conclude? Draw your conclusion based on p-value.                                                                                                              (2 points)
  • Estimate numerically (based on covariance of the stock return with market return and variance of market return) using the sample period data. Obtain beta of the stock from available stock report (refer the financial website that you choose).  Discuss why these three measures are same or different.                                                  (5 points)
  1. Provide interpretation of the coefficient estimate .                                     (4 points)
  2. Comment on model accuracy: standard error and R-square (3 points)
  3. Provide the scatter plot and the fitted line for the linear regression model. (2 points)
  • Discuss as estimated by the fitted model.                                                 (5 points)

 

 

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