available as an in-house course

Course Objectives

Teach you the tools of Crystal Ball Professional and @RISK Professional (the two main commercially available Monte Carlo software packages)
Help you to make optimal decisions despite uncertain conditions
Show you how to communicate the concept of risk to others
Extend your ability to build and analyze spreadsheet risk models
Define VaR and explain how it is used to quantify risk
Measure Value at Risk using various methods (including Monte Carlo)
Familiarize yourself with the profile of major vendors of VaR systems available in the market
Understand the benefits of Stress Testing as a complement to VaR

Course Content

FIRST DAY

Introduction to Monte Carlo simulation
What is risk, why does it matter and how is it measured
What Monte Carlo is, how it was development and how it works
Available products: Crystal Ball, @RISK and integrated products
Integrating Monte Carlo with spreadsheets and software
Deterministic modeling vs. Stochastic modeling
Introduction to risk analysis and simulation techniques

Probability Distributions and Statistics
Why do statistics matter?
Applications in finance
Defining uncertainty through distributions: Overview
Basic Distributions
Basic Statistics

Case Study: Measuring and Using Correlations

Getting Started with Crystal Ball/ @RISK
Launching Crystal Ball/ @RISK
Overview of Crystal Ball/@RISK menu items
Terminology
Navigation
Setting up a Simulation Model

The Critical Importance of Assumptions
Correlating Assumptions
The Normal and Lognormal distributions
Alternate parameter methods
Introduction to other distributions as relevant (e.g. Geometric, Exponential, Weibull, Beta, Gamma, Binomial, Poisson, Discrete distributions)

Case Study: cost estimation and budgeting using a range of distributions

Distribution fitting with BestFit (@RISK)
Use of the DUniform distribution (@RISK)
Correlation analysis
Using alternate parameters
Using a formula as a parameter
Truncating assumption distributions

Case Study: Interest rate distributions in Monte Carlo analysis

Using a function as a distribution
Customizing the distribution gallery
Publishing and subscribing to distributions

Case Study: Real estate price distributions

Approaches to select an appropriate distribution

SECOND DAY

Forecasting
Time series forecasting
Linear regression
Fit a distribution to a forecast
Forecast filtering
Auto extract forecast data
Adding marker lines to forecast statistics

Tools in Crystal Ball/ @RISK
Precision control
User defined macros in Crystal Ball/ @RISK
2D simulation
Bootstrapping
Other functions (e.g. Reports in Excel, Library, Help features, , further sensitivity analysis features, model auditing

Analysis and Presentation of Results
Sensitivity Chart
Overlay Chart
Trend Chart
Report Generation
Saving Results
Simulation Control
Tornado Charts

Case Study: Presenting Monte Carlo to senior management

Interpretation of Simulation Results
Density and cumulative curves
Measures of distributions (mean, standard deviation, skew etc)
Interpretation of simulation results
Use of multiple simulations

Case Study: variations of cost estimation model

Testing the Results
Sampling
Speed
Options
Statistics

Crystal Ball/@RISK Tools
Tornado Charts
Batch Fit
Correlation Matrix
Graphing the Results

OptQuest (Crystal Ball)
What is optimization?
How OptQuest works
Optimization applications
Running OptQuest
OptQuest results
Efficient frontier

Case Study: Application of Monte Carlo to Real Options

THIRD DAY


Introduction to Value at Risk
The concept of Value at Risk
The concept of trading and banking book
The various emerging forms of VaR viz., Component VaR, Property VaR, Liquidity VaR, Earnings-at-Risk
The various methodologies of estimating VaR and their strengths and weaknesses
The comparison between the strength and limitation of VaR

How to calculate Value at Risk
Parametric calculation
Historical simulation
Using Monte Carlo to calculate VaR
Comparing and integrating the methods

The Analytical Framework
Analytical techniques - gap, duration, simulation and value at risk
The concept and assumption under each technique
The comparison and analysis of each technique across various parameters
The role of backtesting

Case Study: Bond prices and VaR

Stress Testing and Volatility Modelling
Stress testing as a complimentary tool to value at risk analysis
Hypothetical and historical scenarios
Integrating stress test scenarios with market risk modelling
The concept of volatility and volatility clustering
Conditional volatility models viz., Exponential Moving Average approach and GARCH
The importance of time errors and the impact of crashes on correlation and its effect on VaR calculation

Case Study: Stress testing a mortgage portfolio

VaR and Regulation
How market risk can be regulated
The purpose of regulatory capital
The various approaches applied to capital charges
Basel II and VaR

Case Study: Regulating the rating agencies after sub-prime – can VaR help?

Implementing VaR in a firm
When is a VaR analysis/stress testing needed
Who can provide the risk management solutions
How to choose a risk management system
Risk-Adjusted performance measurement

Case Study: Liquidity VaR and Long-Term Capital Management

PDF of course outline - Please note that tailoring is possible


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