Numerical Solution of SDE Through Computer Experiments
This is a computer experimental introduction to the numerical solution of stochastic differential equations. A downloadable software software containing programs for over 100 problems is provided at one of the following homepages: http://www.math.uni-frankfurt.de/numerik/kloeden/ http://www.business...
Main Authors: | , , |
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Format: | eBook |
Language: | English |
Published: |
Berlin, Heidelberg
Springer Berlin Heidelberg
1994, 1994
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Edition: | 1st ed. 1994 |
Series: | Universitext
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Subjects: | |
Online Access: | |
Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of Contents:
- 1: Background on Probability and Statistics
- 1.1 Probability and Distributions
- 1.2 Random Number Generators
- 1.3 Moments and Conditional Expectations
- 1.4 Random Sequences
- 1.5 Testing Random Numbers
- 1.6 Markov Chains as Basic Stochastic Processes
- 1.7 Wiener Processes
- 2: Stochastic Differential Equations
- 2.1 Stochastic Integration
- 2.2 Stochastic Differential Equations
- 2.3 Stochastic Taylor Expansions
- 3: Introduction to Discrete Time Approximation
- 3.1 Numerical Methods for Ordinary Differential Equations
- 3.2 A Stochastic Discrete Time Simulation
- 3.3 Pathwise Approximation and Strong Convergence
- 3.4 Approximation of Moments and Weak Convergence
- 3.5 Numerical Stability
- 4: Strong Approximations
- 4.1 Strong Taylor Schemes
- 4.2 Explicit Strong Schemes
- 4.3 Implicit Strong Approximations
- 4.4 Simulation Studies
- 5: Weak Approximations
- 5.1 Weak Taylor Schemes
- 5.2 Explicit Weak Schemes and Extrapolation Methods
- 5.3 Implicit Weak Approximations
- 5.4 Simulation Studies
- 5.5 Variance Reducing Approximations
- 6: Applications
- 6.1 Visualization of Stochastic Dynamics
- 6.2 Testing Parametric Estimators
- 6.3 Filtering
- 6.4 Functional Integrals and Invariant Measures
- 6.5 Stochastic Stability and Bifurcation
- 6.6 Simulation in Finance
- References
- List of PC-Exercises
- Frequently Used Notations