Applied Econometrics - Forecasting - Economics Assignment Help

Assignment Task:

Instructions:
The maximum length for this assessment is ?ve single-sided A4 paper. If you are required to make a decision while conducting a hypothesis test, use a 5% signi?cance level (unless otherwise speci?ed). Be sure to state your null and alternative hypotheses, rejection criterion and your decision. Do not answer questions with a simple ”yes” or ”no”, but carefully justify your answers.
Furthermore, you should upload only one single PDF format ? le. This PDF ?le should consist of both your analysis and codes. In Particular, you should combine both PDF ?les into one PDF ?le. The ?rst PDF ?le contains your written answer that includes your discussion and data analysis, while the other PDF ?le contains the code that you required to generate the ?gures and graphs in your ?rst PDF ? le. 

The following question follows from your Python tutorials from your module. In particular, you should always refer to the codes you have learned there i.e. use similar arrangements to get to the analysis required.
1. Upload all the data available in ’Data For Analysis.xlsx’ onto your Jupyter Notebook. Set the index of the newly uploaded data frame to the Date column, using the command ’set index’. Furthermore, select from this table only two stocks such that each stock is from di?erent industries as well as the Market stock return (’MARKET’ column). Then, clean the Data by removing the Not a number (Nan) data points from your selected series.
2. Plot your data set. Check how the returns behave in exciting periods, such as the market crash of October 1987. Produce a scatter plot of rjt against rmt and comment on the observations. Explain from economics or ?nance knowledge that you acquired what has been going on in these ”remarkable” period(s) you have observed. Furthermore, compare the stocks you have chosen to the stock market as well as the companies in the same industries as your chosen stocks.
3. Use ordinary least squares (OLS) to estimate α and β. Calculate the approximated 95% con?dence intervals for the parameters, and test the null hypotheses, α = 0, and also β ≤ 1 against the alternative hypotheses α 6= 0 & β > 1, respectively. Interpret the result of both tests. Comment on the representations of α and β and their uses throughout ?nance. Furthermore, comment on and justify the outcome of the test based on your selected stock from both statistical and ?nancial perspective. Furthermore, compare your selected stocks to the relative industry and here you may compare them using any method you learned throughout the module you may also reference to any additional readings you have made in this regards relative to your stocks.
4. It is known that the standard deviation of the OLS residuals in CAPM measures the individual risk of the stock, based on your knowledge comment on the reason for this (i.e. why do you think that the standard deviation of the OLS regression represents the individual risk)1. Compute and report your standard deviations (for both stocks) and comment on it. Furthermore, after computing both values (standard deviation of the residuals of both your stocks) perform some analysis that you think is adequate to compare them from a ?nancial as well as statistical framework.

5. Some economists attribute R2 of the regression measures as the proportion of the risk attributable to the market (as opposed to individual factors). Why is that based on your knowledge of regression analysis (goodness of ?t) and CAPM? Comment on the values you observe when computing the R2 of both of your stock returns.
6. Use the Chow stability test to check whether the model is stable over the full period of the sample at hand. Comment on the ?ndings and justify with reference to news and other published articles what had actually happened to your stock at this particular event (special occasion)? For example, is there a di?erence between di?erent stocks when it comes to Chow test, if so, why is that based on your econometric and economics or ?nance knowledge?

7. In this exercise, you are requested to test the strict CAPM against the alternative of the Arbitrage Pricing Model (APM) by testing the ability of macroeconomic variables to predict returns. To do so, we will require the following sequential steps to be applied to get your data sorted:
(a) Compute the rate of in?ation (RINF), the growth in industrial production (GIND), and changes in the real oil price (ROIL) which are essentially the percentage monthly changes in CPI, FRBIND, and POIL/CPI, respectively.
(b) Add the computed variables to your initial Dataframe df.
(c) Comment on the ?nancial reasoning behind diving the POIL by CPI. Why it is important to do so? (d) Select the following columns [0Stock10,0Stock20,0MARKET0,0RINF0,0GIND0,0ROIL0] from your updated Dataframe df and call it df1.
(e) You will ?nd that in your new Dataframe there are elements that are not a number (NaN). Delete these elements using df1 = df1.dropna().
In this exercise, you will have to innovate in many perspectives. For example, perform a general ?t of a model, comment on the statistical signi?cance of the variable empirically, do you think the CAPM was better than the APM in modeling the returns of your chosen stocks? Here, you can do lots of issues and you need to be deep in your ideas. Please mention why are you doing the thing you are doing as you go through your Jupyter notebook comments.

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