scenario-analysis

  1. Nicole Seaman

    P1.T4.24.8 Monte Carlo simulations, loss frequency data issues, and scenario analysis

    Learning Objectives: Explain how a loss distribution is derived from an appropriate loss frequency distribution and loss severity distribution using Monte Carlo simulations. Describe the common data issues that can introduce inaccuracies and biases in the estimation of loss frequency and...
  2. Nicole Seaman

    P1.T4.810. Spectral risk measures, especially Expected Shortfall (ES) (Dowd Ch.2)

    Learning objectives: Explain and calculate Expected Shortfall (ES), and compare and contrast VaR and ES. Describe spectral risk measures, and explain how VaR and ES are special cases of spectral risk measures. Describe how the results of scenario analysis can be interpreted as coherent risk...
  3. Nicole Seaman

    P1.T4.806. Putting value at risk (VaR) to work (Allen Ch.3)

    Learning objectives: Describe the limitations of the delta-normal method. Explain the full revaluation method for computing VaR. Compare delta-normal and full revaluation approaches for computing VaR. Explain structured Monte Carlo, stress testing, and scenario analysis methods for computing...
Top