Question 1 (Mandatory) (10 points) Use the daily data from MSFT below Price=MSFT adjusted close price ret = the log return A. Discuss stationarityonstationarity behavior of the two series from their graphs and ACF functions. price 2007-06-01/2018-08-24 100 100 80 80 60 60 40 20 20 Jun 01 2007 Dec 01 2008 Jun 01 2010 Dec 01 2011 Jun 03 2013 Dec 01 2014 Jun 01 2016 Dec 01 2017 ret 2007-06-01 / 2018-08-24 0 15 015 010 0:10 005 0.05 000 0.00 005 0.05 010 -0.10 Jun 01 2010 Dec 01 2011 Dec 01 2014 Jun 01 2007 Dec 01 2008 Jun 03 2013 Jun 01 2016 Dec 01 2017 Series price Series price ACE 0.0 04 0.8 2 4 6 10 Lag Series ret ACF -0.06 0.00 2 4 6 8 10 Lag . Autocorrelations of series 'price', by lag 1 2 3 4 5 6 7 8 9 10 0.998 0.995 0.993 0.991 0.989 0.986 0.984 0.982 0.980 0.978 Autocorrelations of series 'ret', by lag 4 2. 3 5 9 7 1 6 8 10 0.039 -0.056 -0.043 -0.065 -0.058 0.033 0.008 -0.038 -0.024 -0.009 B. The Augmented Dickey Fuller (ADF) tests for MSFT price and returns are reported below. Test unit root (nonstationarity) hypothesis for each series. library(tseries) #incorporates a constant and a linear trend adf.test(price) Augmented pickey-Fuller Test data: price Dickey-Fuller = 1.291, Lag order = 14, p-value = 0.99 alternative hypothesis: stationary adf.test(ret) Augmented pickey-Fuller Test data: ret Dickey-Fuller = -14.405, Lag order = 14, p-value = 0.01 alternative hypothesis: stationary