- choose a single stock form the index, and compute the corresponding daily, monthly and annual returns, for each sampling frequency, test whether the returns are normally distributed. Include appropriate graphs in your final report and ensure that you clearly explain your analysis in your own words.
- Compute the Jarque-Bera test statistic and p-values for all stocks in the index over daily, monthly and annual frequencies. Summarise your results in a single graph which shows three side-by-side boxplots of the test statistic comparing: daily, monthly and annual return distributions.
- Download data for the S&P 100 index, and repeat exercise 1 with this data.
- Estimate the alpha and Beta of each stock according to the single-index model using OLS regressions over monthly excess returns, staring your results in a data frame. Produce a single graph showing side-by-side boxplots summarsing the distribution of the alpha and beta coefficients over all stocks in the index.
- Compute the covariance matrix of components of the index using the monthly returns. According to the single-index model, the covariance of a given pair of stocks should be directly proportional to the product of the corresponding Beta values. Test this hypothesis, and summarise your results in a single graph.
这个需要用Python，进行编程，利用OLS regression 模型，作图，并且计算得出要求的数据，然后进行分析，测试等。
如何做好Python项目作业 - 优易论文