1. Learn
    2. /
  2. Courses
    3. /
  3. Intro to Computational Finance with R
  • 1

    Return calculations

    Learn how to calculate, analyze and plot simple and continuously compounded returns in R.

  • 2

    Random variables and probability distributions

    Learn how to work with probability distributions in R in the context of return and value-at-risk calculations.

  • 3

    Bivariate distributions

    Explore bivariate probability distributions in R.

  • 4

    Simulating time series data

    Learn how to use R to simulate autoregressive and moving average processes.

  • 5

    Analyzing stock returns

    Learn how to analyze stock returns with the R packages PerformanceAnalytics, zoo and tseries.

  • 6

    Constant expected return model

    Estimate parameters of the constant expected return (CER) model, compute standard errors and confidence intervals and test various hypotheses about the parameters and assumptions of the model. Perform bootstrapping of CER model estimates.

  • 7

    Introduction to portfolio theory

    Compute portfolios that consist of Boeing and Microsoft, T-bills and Boeing, T-bills and Microsoft and T-bills and combinations of Boeing and Microsoft. Use R functions to compute the global minimum variance portfolio and the tangency portfolio.

  • 8

    Computing efficient portfolios using matrix algebra

    Using the monthly closing price data on four Northwest stocks, you will estimate expected returns, variances and covariances to be used as inputs to the Markowitz algorithm. You will compute the global minimum variance portfolio, efficient portfolios, and the tangency portfolio for short-sales allowed and for short-sales not allowed.

Initializing

Exercise

Normality of the asset returns

Remember that the Constant Expected Return model assumes that returns are normally distributed. Is that a reasonable assumption? The Jarque Bera test provides a way to answer that question. You can easily perform the Jarque Bera test for normality in R with the jarque.bera.test function.

Let us say that you want to investigate whether it is reasonable that the returns of VBLTX are normally distributed. More formally, you would like to test the null hypothesis:

$$H_0: r_{it} \sim \text { normal vs. } H_1: r_{it} \sim \text{ not normal }$$

using a 5% significance level, with \(i = \mathrm{VBLTX}\).

Instructions

100 XP

Use the jarque.bera.test to test the normality of the "VBLTX" returns and print the output to the console. What do you conclude?