answer those two questions and i will provide dataset later

(d) Given the stock prices {yt, t = 1,...T}, the stock returns are defined as It = log Yt-1 yt ,t=2, ....T. Write Python script to compute the stock returns and produce their time series plot. Com- ment on this plot in conjunction with the plot of the prices {y, t = 1, ..., T}. Include the plot and the Python script in your submission. Calculate the descriptive statistics for the time series rt: max, min, sample mean, sample variance, kurtosis and skewness. Comment on these values. (e) For the return dataset {rz} Use the last 100 observations as testing data, and the previous observations for the training data. Use the training dataset to estimate the parameters (weight a and initial level lo. You may set lo to be the first observation or the average of a few first observations) of the SES method. Based on these estimates of a and lo, compute one-ahead-forecasts on the test data fe|t-1. Compute the Mean Absolute Percentage Error (MAPE) and plot the forecasts. Please also include your Python code in submission. (d) Given the stock prices {yt, t = 1,...T}, the stock returns are defined as It = log Yt-1 yt ,t=2, ....T. Write Python script to compute the stock returns and produce their time series plot. Com- ment on this plot in conjunction with the plot of the prices {y, t = 1, ..., T}. Include the plot and the Python script in your submission. Calculate the descriptive statistics for the time series rt: max, min, sample mean, sample variance, kurtosis and skewness. Comment on these values. (e) For the return dataset {rz} Use the last 100 observations as testing data, and the previous observations for the training data. Use the training dataset to estimate the parameters (weight a and initial level lo. You may set lo to be the first observation or the average of a few first observations) of the SES method. Based on these estimates of a and lo, compute one-ahead-forecasts on the test data fe|t-1. Compute the Mean Absolute Percentage Error (MAPE) and plot the forecasts. Please also include your Python code in submission