1. Using the past 1 year of adjusted closing prices of NFLX, calculate and plot the kernel density function of daily differences between adjusted closing prices.
2. Find the 0%, 5%, 25%, 50%, 75%, 95% and 100% quantiles of daily differences of adjusted closing prices for question 1.
3. Calculate the experimental cumulative density function for question 2 and plot the ogive.
4. Test whether the distribution from question 3 is normal. Can one reject the null hypothesis of normality at significance level 0.05?
5. Find the Pearson correlation coefficient between the adjusted daily closing price of NFLX in 2014 and adjusted daily closing price of NFLX for: one trading day prior; two trading days prior etc. up until 20 trading days prior, and plot these in a graph. What does the graph indicate?
6. Using KS-test, find whether the distribution of daily differences of adjusted closing NFLX prices in 2014 was significantly different from the same distribution for 2012.
7. Determine whether the daily volatility of GOOG is equal to to volatility of NFLX by comparing index X=log(High/Close) for latest 250 daily prices for both stocks. Treat X as a sample from a much larger population, for both stocks. Use the significance level of 0.05 in each test.
A) Assume normality of both distributions.
B) Do not assume normality; instead, use the Wilcoxon non-parametric two-sample test.
C) Do not assume normality; instead, use the two-sample Kolmogorov-Smirnov test.
8. Assuming normality, test the null hypotheses for latest 250 closing prices of NFLX on New York Stock Exchange
where Xi=log(Close(i)/Close(i-1)), i=2,3,..,250
Can either of hypotheses be rejected at significance level 0.01?
10. Find the Spearman, Pearson and Kendall correlation between the latest 500 daily closing prices of SU.TO and SU stocks. What do the correlations indicate?
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