Exercise

# Can't Forecast White Noise

A white noise time series is simply a sequence of uncorrelated random variables that are identically distributed. Stock returns are often modeled as white noise. Unfortunately, for white noise, we cannot forecast future observations based on the past - autocorrelations at all lags are zero.

You will generate a white noise series and plot the autocorrelation function to show that it is zero for all lags. You can use `np.random.normal()`

to generate random returns. For a Gaussian white noise process, the mean and standard deviation describe the entire process.

Plot this white noise series to see what it looks like, and then plot the autocorrelation function.

Instructions

**100 XP**

- Generate 1000 random normal returns using
`np.random.normal()`

with mean 2% (0.02) and standard deviation 5% (0.05), where the argument for the mean is`loc`

and the argument for the standard deviation is`scale`

. - Verify the mean and standard deviation of returns using
`np.mean()`

and`np.std()`

. - Plot the time series.
- Plot the autocorrelation function using
`plot_acf`

with`lags=20`

.