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  3. Quantitative Risk Management in R
  • 1

    Exploring Market Risk-Factor Data

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    In this chapter, you will learn how to form return series, aggregate them over longer periods and plot them in different ways. You will look at examples using the qrmdata package.

  • 2

    Real World Returns are Riskier Than Normal

    In this chapter, you will learn about graphical and numerical tests of normality, apply them to different datasets, and consider the alternative Student t model.

  • 3

    Real World Returns are Volatile and Correlated

    In this chapter, you will learn about volatility and how to detect it using act plots. You will learn how to apply Ljung-Box tests for serial correlation and estimate cross correlations.

  • 4

    Estimating Portfolio Value-at-Risk (VaR)

    In this chapter, the concept of value-at-risk and simple methods of estimating VaR based on historical simulation are introduced.

Initializing

Ubung

Testing normality for longer time horizons

As returns are added together over longer time periods, a central limit effect takes place and returns tend to become more normal.

In this exercise, you will use aggregation functions that you learned in the first chapter to aggregate the data in djx_d, containing the daily log-returns for 29 of the Dow Jones stocks for the period 2000-2015. Then, you'll apply the Jarque-Bera test to the daily, weekly and monthly returns. djx_d is loaded in your workspace.

Anweisungen

100 XP
  • Calculate weekly and monthly log-returns of djx_d and assign to djx_w and djx_m, respectively.
  • Fill in apply() to calculate the p-value of the Jarque-Bera test for each of the Dow Jones daily return series in djx_d.
  • Do the same for the weekly equity returns in djx_w.
  • Do the same for the monthly equity returns in djx_m.