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这是一篇来自英国的关于运用Python解决下列相关变量指数的Python代写

Case Study 1

The attached csv file (‘stocks_.csv’) contains historical time series for different variables until the end of September 2022. The variables are two equity indices of S&P 500 (column ‘SPX’) and NASDAQ Composite Index (column ‘IXIC’), the VIX index (column ‘VIX’) and the yield rate on 1-Month U.S. Treasury Bill (column ‘YIELD 1M’). The figures for equity indices and VIX are reported in index level points while the yield is reported in percentage points.

Required:

Complete the following tasks providing your codes in a single Python file, and a report with your answers to Question 1 to Question 3:

1) Detect potential outliers for each time series based on a simple methodology. You are free with respect to the methodology you choose and based on which criterion you define outliers. Report on why you made that choice of methodology and establish its potential assumptions.[40 marks]

2) Build a linear quarterly model using the observations from the past 20 years that explains

the VIX index by one or more other variables provided.

a.Motivate your choice of independent variables and include the equation in your report along.[20 marks]

b.Comment whether your results are prone to multicollinearity.[10 marks]

c.Provide 𝑅 2 and Mean Squared Forecasting Error for your model’s performance (in sample).[10 marks]

3) Give an estimate of the VIX index level in a scenario where S&P 500 drops to 2,500 at the end of the last quarter of 2022 (i.e., the level of S&P 500 is 2,500 at date 31/12/2022). Same question if Nasdaq Composite increases to level 15,000 at date 31/12/2022.[20 marks]