Monte Carlo - Methodologies and Applications for Pricing and Risk Management

Day One

09.00 - 09.15 Welcome Address

09.15 - 12.00 Introduction to Monte Carlo Simulation

• What is "Monte Carlo Simulation"?
• Advantages/Disadvantages of MCS
• Applications of Monte Carlo Simulation in Finance
• A Couple of Examples of What You Can Do
• Introductory Exercise

The Monte Carlo Toolkit

• Generating Random Numbers
  • Random number generators - how they work
  • Testing the Excel/VB random number generator

12.00 - 13.00 Lunch

13.00 - 16.30 The Monte Carlo Toolkit (cont' d)

• Statistical Distributions
  • Uniform, normal and log-normal distributions
  • Binomial distribution
  • Other distributions
• Sampling Techniques
  • Generating normally distributed random numbers
  • Drawing form multivariate distributions
• Stochastic Differential Equations
• Exercises

Day  Two

09.00 - 09.15 Recap

09.15 - 12.00 Pricing Options Using Monte Carlo Simulation

• Overview of Option Pricing Models
• Pricing Standard European Options
• Pricing "Path Dependent" Options
  • Barrier options
  • Lookback
  • Asian
  • Range Floaters/EARNs
• Pricing American Options
• Greeks in Monte Carlo
• Exercises/Workshop

12.00 - 13.00 Lunch

13.00 - 16.30 Calculating "Value-at-Risk"

• What is "Value-at-Risk"?
  • VaR due to market risk
  • VaR due to credit risk
• Approaches to Calculating VaR
• Calculating VaR Using Monte Carlo Simulation
  • VaR for Single Asset Portfolios
  • Formulating the price process
  • Discretising the price process
  • Constructing the P&L Histogram
  • Inferring the VaR
• Exercises

Day  Three

09.00 - 09.15 Recap

09.15 - 12.00 Calculating Value-at-Risk (continued)

• VaR for Multiple Asset Portfolios
  • When prices are independent
  • When prices are perfectly correlated
  • When prices are imperfectly correlated
  • Choleksky decomposition
  • Constructing the P&L Histogram
  • Inferring the VaR
• Stress Testing
• Exercises/Workshop

12.00 - 13.00 Lunch

13.00 - 16.00 Making Monte Carlo Simulation More Efficient

• Problems with Conventional MCS
  • "Clustering" and other problems
• Quasi-Monte Carlo Approaches
• Scrambled Nets Approach
• Scenario Simulation - an Alternative Approach
• Examples and Exercises

16.00 - 16.30 Recap, Evaluation and Termination of the Seminar