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|a 10.1057/978-1-137-03351-2
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| 100 |
1 |
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|a Belomestny, Denis,
|e author.
|0 http://id.loc.gov/authorities/names/no2015006851
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| 245 |
1 |
0 |
|a Advanced simulation-based methods for optimal stopping and control :
|b with applications in finance /
|c Denis Belomestny, John Schoenmakers.
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| 264 |
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1 |
|a London, United Kingdom :
|b Palgrave Macmillan,
|c [2018]
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| 300 |
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|a 1 online resource
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| 336 |
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|a text
|b txt
|2 rdacontent
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| 337 |
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 347 |
|
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|a text file
|b PDF
|2 rda
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| 504 |
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|a Includes bibliographical references and index.
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| 588 |
0 |
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|a Vendor-supplied metadata.
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| 505 |
0 |
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|a Intro; Acknowledgements; Contents; List of Figures; List of Tables; 1 Introduction; Part I Monte Carlo Techniques; 2 Elementary Monte Carlo Methods; 2.1 High-Dimensional Integration; 2.1.1 Numerical Evaluation on a Deterministic Grid; 2.1.2 Alternative: Monte Carlo Simulation; 2.1.3 Applications to Option Pricing; 2.2 Simulation of Random Variables; 2.2.1 Inverse Transform Method; 2.2.2 Box-MÃơller Method for Standard Normal r.v.; 2.2.3 Standard Normals by Marsaglia's Method; 2.2.4 Simulation of a General Density; 2.2.5 Variance Reduction.
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| 505 |
8 |
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|a 2.3 Monte Carlo Construction of Stochastic Differential Equations (SDEs)2.3.1 Simulating European Options; 3 Variance Reduction for SDEs; 3.1 Introduction; 3.2 Control Variates for Strong Approximation Schemes; 3.2.1 Series Representation; 3.2.2 Integral Representation; 3.3 Regression Analysis; 3.3.1 Global Monte Carlo Regression Algorithm; 3.3.2 Piecewise Polynomial Regression; 3.3.3 Summary of the Algorithm; 3.4 Complexity Analysis; 3.4.1 Integral Approach; 3.4.2 Series Approach; 3.4.3 Discussion; 3.5 Numerical Results; 3.5.1 One-Dimensional Example; 3.5.2 Five-Dimensional Example.
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| 505 |
8 |
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|a 4 Multilevel Methods4.1 Introduction; 4.2 Euler Scheme for Lévy-Driven SDEs; 4.3 Multilevel Path Simulation for Weak Euler Schemes; 4.3.1 Coupling Idea; 4.3.2 MLMC Algorithm; 4.4 Examples; 4.4.1 Diffusion Processes; 4.4.2 Jump Diffusion processes; 4.4.3 General Lévy Processes; 4.5 Numerical Experiments; 4.5.1 Diffusion Process; 4.5.2 Jump Diffusions; Part II Primal Methods for Optimal Stopping and Control; 5 General Problem Setups; 5.1 Optimal Stopping; 5.2 Generalization to Markovian Control Problems; 6 Primal Approximation Methods for Optimal Stopping; 6.1 Notation.
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| 505 |
8 |
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|a 6.2 Methods Based on Dynamic Programming Principle6.3 Simulation-Based Optimization Algorithms; 6.4 Convergence Analysis; 6.5 Complexity Analysis; 7 Stochastic Policy Iteration Methods; 7.1 Policy Improvement, Iteration, and Stability; 7.2 Multilevel Simulation Based Policy Iteration; 7.2.1 Introduction; 7.2.2 Policy Iteration for Optimal Stopping; 7.2.3 Simulation Based Policy Iteration; 7.2.4 Standard Monte Carlo Approach; 7.2.5 Multilevel Monte Carlo Approach; 7.2.6 Numerical Comparison of the Two Estimators; 7.2.7 Numerical Example: American Max-Call; 7.2.8 Proofs.
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| 505 |
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|a 7.2.9 Proof of Proposition 557.2.10 Proof of Theorem 58; 7.2.11 Proof of Theorem 59; 7.2.12 Appendix; 8 Regression Methods for Markovian Control Problems; 8.1 Setup Based on Reference Measure; 8.1.1 Algorithms Based on Local Estimators; 8.1.2 Global Regression Estimators; 8.2 Convergence Analysis of Regression Methods; 8.2.1 Convergence of Local Regression Estimators; 8.2.2 Convergence of Global Regression Estimators; 8.3 Dual Upper Bounds; 8.4 Numerical Example; 8.4.1 Some Results from the Theory of Empirical Processes; Part III Dual Methods for Optimal Stopping and Control.
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| 520 |
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|a This is an advanced guide to optimal stopping and control, focusing on advanced Monte Carlo simulation and its application to finance. Written for quantitative finance practitioners and researchers in academia, the book looks at the classical simulation based algorithms before introducing some of the new, cutting edge approaches under development.--
|c Provided by publisher.
|
| 650 |
|
0 |
|a Stochastic differential equations.
|0 http://id.loc.gov/authorities/subjects/sh85128177
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| 650 |
|
0 |
|a Stochastic processes.
|0 http://id.loc.gov/authorities/subjects/sh85128181
|
| 650 |
|
0 |
|a Finance
|x Mathematical models.
|0 http://id.loc.gov/authorities/subjects/sh85048260
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| 650 |
|
7 |
|a MATHEMATICS
|x Applied.
|2 bisacsh
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| 650 |
|
7 |
|a MATHEMATICS
|x Probability & Statistics
|x General.
|2 bisacsh
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| 650 |
|
7 |
|a Finance
|x Mathematical models.
|2 fast
|0 (OCoLC)fst00924398
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| 650 |
|
7 |
|a Stochastic differential equations.
|2 fast
|0 (OCoLC)fst01133506
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| 650 |
|
7 |
|a Stochastic processes.
|2 fast
|0 (OCoLC)fst01133519
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| 655 |
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4 |
|a Electronic books.
|
| 700 |
1 |
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|a Schoenmakers, John,
|e author.
|0 http://id.loc.gov/authorities/names/n2004154986
|
| 776 |
0 |
8 |
|i Printed edition:
|z 9781137033505
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| 903 |
|
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| 929 |
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|t Library of Congress classification
|a QA274.23
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|g ebooks
|i 12551470
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