Exercise One: Using the Logarithmic & Polynomial Transformation (Module) and (Video Lecture) as your guide, (a) use Yahoo Finance to download the last five years of data using monthly frequency for the Implied Volatility Index from CBOE (Ticker: ^VIX), (b) filter your data to arrange it from newest to oldest in the bottom, (c) transform the time series by using a logarithmic transformation, (d) as shown in today's class, use the FORECAST() function and Euler's Constant to predict the value for the index on the months of October, November, and December, (e) graph your ^VIX series, and add a polynomial regression, (f) use the polynomial regression equation to forecast the values for the index for the aforementioned months in part (d), (g) what model gives you the most reliable forecast and how do you quantify this? (h) What is the VIX index and why is it so important? (Hint: Please make sure your data is consistent to show only the beginning of each month).