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Joined: Jan 2003
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Wed Jan 09, 13 02:10 PM
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Monte Carlo Simulation in Finance: Models, Algorithms and Practice with Application to Derivatives Pricing, Risk Measures and CVA by Jörg Kienitz
Frankfurt: 29th & 30th April 2013
Download Pdf
Discount Structure:
Register to both days of the workshop and receive a ?200 discount
Book before 22nd February 2013 to receive a 20% early bird discount
Book before 22nd March 2013 to receive a 10% early bird discount
Day 1: Monte Carlo Simulation in Finance
Mathematical Basics
Foundations of Probability
How does Monte Carlo Work?
Distributions
o Basic Distributions in Finance
Stochastic Processes
o Diffusion Processes
o Jump-Diffusion Processes
o Jump Processes
Applications of the Monte Carlo Method
Option Pricing
Evaluating Hedge Strategies
Scenario Generation and Risk Measures
Static Monte Carlo Simulation
Sampling from the Uniform Distribution
o Random Number Generators
o Good ones and bad ones
Sampling Techniques
o Inverse Method
o Ratio of Uniforms
Sampling from the Normal and other Distributions
Dynamic Monte Carlo Simulation
Path Generation Methods
o (Log) Euler-Scheme
o Predictor Corrector
o Bridge Sampling
o Exact Sampling
Sampling from Jump Diffusion Processes
o SGS Sampling
o FGS Sampling
o Example: Merton Model
Sampling from Stochastic Volatility Models
o Heston
o SABR
Sampling from Pure Jump Processes
o Variance Gamma, NIG
o Stochastic Volatility Lévy Models
Day 2: Monte Carlo Simulation in Finance
CVA - Simulating Future Interest Rate
Simulating Short Rate Processes
o Hull-White
o CIR
Simulating Market Models
o Libor Market Models
Calculating CVA for Fixed Income Products
Speeding up and improving your Monte Carlo
Variance Reduction Techniques
o Antithetic Sampling
o Control variates
o Importance Sampling
o Stratification
Quasi Random Numbers
o Halton Sequence
o Sobol Sequence
Multi-Level Monte Carlo
Simulating Multi-Dimensional Models
Introducing Dependence
o Correlation
o Copula
Scenario Generation and Risk Measures (Calculating CVaR using Simulation)
Multi-Dimensional Normal, Variance Gamma Models or NIG Models
Greeks (Adjoint, Proxies) and Early Exercise
The Adjoint Method
The Proxy Method
American and Bermudan Options
Illustration in the Libor Market Model contents
How to apply Adjoints to higher order Greeks
Implementation Issues (from Algorithms to Code)
Ingredients for a successful implementation of Monte Carlo algorithms
Choosing a language (VBA, MatLab, C++, C#)
Designing algorithms
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