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sampling-error

  1. Nicole Seaman

    P1.T2.21.6 Bootstrapping and antithetic/control variates

    Learning objectives: Explain the use of antithetic and control variates in reducing Monte Carlo sampling error. Describe the bootstrapping method and its advantage over Monte Carlo simulation. Describe pseudo-random number generation. Describe situations where the bootstrapping method is...
  2. Nicole Seaman

    P1.T2.21.5 Monte Carlo simulation

    Learning objectives: Describe the basic steps to conduct a Monte Carlo simulation. Describe ways to reduce Monte Carlo sampling error. Questions: 21.5.1. Mary wants to approximate the expected value of an option. She conducts a Monte Carlo simulation and based on her initial sample size, n =...
  3. Nicole Seaman

    P1.T2.601. Variance reduction techniques (Brooks)

    Learning objectives: Explain how to use antithetic variate technique to reduce Monte Carlo sampling error. Explain how to use control variates to reduce Monte Carlo sampling error and when it is effective. Describe the benefits of reusing sets of random number draws across Monte Carlo...
  4. Nicole Seaman

    P1.T2.600. Monte Carlo simulation, sampling error (Brooks)

    Learning objectives: Describe the basic steps to conduct a Monte Carlo simulation. Describe ways to reduce Monte Carlo sampling error. Questions: 600.1. Although simulation methods might be employed in each of the following situations (or "use cases"), which situation below LEAST requires the...
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