We emphasized the importance of recognizing whether time is a fundamental component of data, and we suggested a simple plot, illustrated in Figure 2.3 to use to decide how important time may be.

Generally, we know that price data on a stock are highly dependent on time, but the returns on a stock are not so dependent on time, although their frequency distribution may vary over time.

(a) For the NFLX daily closes in the first calendar quarter of 2018 , produce a scatterplot of the lagged daily prices versus the prices. 

Are these data similar to a random walk?

(b) Produce a scatterplot of the lagged daily returns versus the returns of NFLX for the first calendar quarter 2018. Are these data similar to a random walk? Do there appear to be other time dependencies?

Transcribed Image Text:

Cumulative Proportionate Population 0.0 0.2 0.4 0.6 0.8 1.0 Lorenz Curves 0.0 0.2 0.4 0.6 0.8 1.0 Cumulative Proportionate Amount FIGURE 2.3 Lorenz Curves Corresponding to ECDFs of Skewed Data