Next:
Contents
Contents
Index
Titletit
Discrepancy and Discrepancies in Monte Carlo
Mark Tenney
Date:
June 16, 2003
1
Contents
For users of Monte Carlo
Introduction
Monte Carlo in Finance
Option Pricing
Interest Rate Models
Insurance Applications
Monte Carlo
Quasi-Random Monte Carlo
Discrepancy
Variation of a Function
Discrepancies
One simulation method is as good as another?
Simulation methods are black boxes?
Error bound of Monte Carlo is
?
Discrepancy has no relation to Monte Carlo?
Low discrepancy just another tool?
Better error bounds than low discrepancy can be invented?
Better methods than low discrepancy can be invented?
With Simulation Any Method Goes?
We don't compute integrals?
Cajun Wisdom
Its the Discrepancy
Part of Integration Theory
Its all integration
CTE for segregated fund guarantees is a good application
Discrepancy measures an integration method
QRMC-LDS provides value even for small sets of points
Variation on puts
Set up with Bounded Variation
Its the Theorems
Experience is good in many cases
Theorems
Discrepancy
Variation
One Dimension
Theorems on Variation
Hardy Krause variation n-dimensions
Monte Carlo Expected Error
Koksma's Inequality
Koksma-Hlawka's Inequality
Niederreiter Theorem 2.12
European Put
Variation
Koksma Inequality
Some URLs
Boyle
Brender
Broadie
Brown
Carothers
Craighead
Embrechts
Hakansson
Hancock
Hardy
Manistre
Niederreiter
Panjer
Papageorgiou
Elias Shiu
Ken Seng Tan
Tenney
Traub
Bibliography
Index
About this document ...
Owner 2005-08-14
?