Monte Carlo simulation

Monte Carlo simulation

1. The technique was initially developed by Stanislaw Ulam

2. This simulation was named after the gambling destination in Monaco

3. This simulation is a model used to predict the probability of a variety of outcomes

4. Help to explain the impact of risk and uncertainty in prediction and forecasting models.

5. A Monte Carlo simulation is used to tackle a range of problems in many fields

   • Investing
   • Business
   • Physics
   • Engineering
   • Finance
   • Meteorology
   • Astronomy
   • Particle physics

6. The Monte Carlo Simulation instead uses multiple values and then averages the results.

7. HOW DOES THIS METHOD WORK ?

   • This method acknowledges an issue for any simulation technique
     • A Monte Carlo simulation focuses on constantly repeating random samples
       • Then, a Monte Carlo simulation takes the variable that has uncertainty and assigns it a random value.
         • This process is repeated again and again while assigning many different values
           • Once the simulation is complete, the results are averaged to arrive at an estimate.

8. MONTE CARLO SIMULATION STEPS

   • STEP 1
     • Use the historical price data of the asset to generate a series of periodic daily returns using the natural logarithm
   • STEP 2
     • Next use the AVERAGE, STDEV.P, and VAR.P functions on the entire resulting series
   • STEP 3
     • Next, obtain a random input
       • The equation for the following day's price is:
   • STEP4
     • To take e to a given power x in Excel, use the EXP function: EXP(x). Repeat this calculation the desired number of times. (Each repetition represents one day.)

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