Testing FX returns for normality

So far, the exercises in this chapter have examined the normality of equity index returns and individual equity returns.

To reinforce these ideas, you will apply similar ideas to exchange-rate log-returns. The dataset fx_d contains daily log-returns of the EUR/USD, GBP/USD and JPY/USD exchange rates for the period 2001-2015, and the dataset fx_m contains the corresponding monthly log-returns. Both are multivariate; they are loaded into your workspace.

Which of the monthly log-return series appears the most normal?

This exercise is part of the course

Quantitative Risk Management in R

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Exercise instructions

Plot the daily exchange-rate log-return series in fx_d with the appropriate plotting function.
Use apply() to conduct the Jarque-Bera test on each of the series in fx_d.
Plot the monthly log-return series in fx_m with the same plotting function and parameter type = "h".
Use apply() to conduct the Jarque-Bera test on each of series in fx_m.
Fill in apply() to fit a Student t distribution to each of the series in fx_m and obtain the parameter estimates.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Plot the daily log-return series in fx_d
___(___)

# Apply the Jarque-Bera test to each of the series in fx_d
___(___)

# Plot the monthly log-return series in fx_m
___(___)

# Apply the Jarque-Bera test to each of the series in fx_m
___(___)

# Fit a Student t distribution to each of the series in fx_m
apply(___, ___, function(v){fit.st(v)$par.ests})

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