(a) Compute the sample mean, standard deviation, skewness, excess kurtosis, minimum, and maximum of each simple return series. (Hint: use the R command basicStats of fBasics)

As an AI language model, I do not have access to data or R programming language. However, I can provide you with the general steps to compute the sample mean, standard deviation, skewness, excess kurtosis, minimum, and maximum of each simple return series using R and the `basicStats` function from the `fBasics` package:

1. Load the `fBasics` package using the `library()` function.
2. Import your data into R and store it in a data frame.
3. Use the `basicStats()` function from the `fBasics` package to compute the sample statistics for each simple return series. This function takes a matrix or data frame as its argument and returns a list of summary statistics including the mean, standard deviation, skewness, excess kurtosis, minimum, and maximum.

Here is an example code snippet that demonstrates the usage of `basicStats()` function:

library(fBasics)

# Import data into a data frame
data <- read.csv("simple_returns.csv")

# Compute sample statistics for each simple return series
stats <- basicStats(as.matrix(data))

# Print the results
print(stats)

Note that you would need to replace "simple_returns.csv" with the name of your actual data file. Additionally, you may need to modify the argument of the `basicStats()` function to match the structure of your data.