Analyze the raw data of Arçelik and LG companies with the same date observation interval (1000 days) according to the following steps.
Use R studio or Python.
1. Give general information about the selected financial assets.
2. Plot the prices of selected financial assets as a time series.
3. Calculate and plot the simple returns (daily) of the selected financial assets.
4. Calculate and plot the empirical simple return distributions (monthly, weekly and daily) of the selected financial assets. How to calculate weekly and monthly return series from daily price data?
5. Plot the bar (histogram) and normal distribution density graphs of the daily simple returns of the selected financial assets on the same graph.
6. Check that the daily, weekly and monthly returns of the selected financial assets conform to the Normal distribution.
7. Prepare the comparison table of the daily returns of the selected financial assets.
8. Estimate and interpret the regression equations of the daily returns of the selected financial assets. (Yt=a+bXt+utveXt=c+dYt+et)
9. Draw and interpret the autocorrelation functions (ACF) of the daily returns of the selected financial assets.
10. Model daily returns with AR(1) and MA(1) models. Draw and interpret the autocorrelation graphs (ACF) of the residuals. Discuss the suitability of AR(1) and/or MA(1) models for the yield series.
11. The solution to the last two problems can be easily found with standard econometrics programs with a single command (R-project) or a single menu selection (in Eview, GRETL and similar programs).
12. Plot and interpret the autocorrelation functions (ACF) of the squares of the daily returns of the selected financial assets.
13. Model the daily returns of the selected financial assets with the VAR(1) - Vector Autoregressive(1) model and draw and interpret the impulse-response functions (IRF).
14. Model the daily returns of the selected financial assets with the GARCH(1,1) model and interpret the results. (The solution to this problem can be easily found with standard econometrics programs with a single command (R-project) or a single menu selection (in Eview, GRETL and similar programs).)
15. Other analyzes you have made other than those above that you find worthy of reporting.
