Advanced Simulation-Based Methods for Optimal Stopping and Control (inbunden)
Format
Inbunden (Hardback)
Språk
Engelska
Antal sidor
364
Utgivningsdatum
2018-02-13
Upplaga
1st ed. 2018
Förlag
Palgrave Macmillan
Illustratör/Fotograf
Bibliographie
Illustrationer
14 Illustrations, black and white; XVI, 364 p. 14 illus.
Dimensioner
242 x 162 x 26 mm
Vikt
681 g
Antal komponenter
1
Komponenter
1 Hardback
ISBN
9781137033505
Advanced Simulation-Based Methods for Optimal Stopping and Control (inbunden)

Advanced Simulation-Based Methods for Optimal Stopping and Control

With Applications in Finance

Inbunden Engelska, 2018-02-13
1269
  • Skickas inom 7-10 vardagar.
  • Gratis frakt inom Sverige över 159 kr för privatpersoner.
  • Köp nu, betala sen med
Finns även som
Visa alla 1 format & utgåvor
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.
Visa hela texten

Passar bra ihop

  1. Advanced Simulation-Based Methods for Optimal Stopping and Control
  2. +
  3. Levy Matters IV

De som köpt den här boken har ofta också köpt Levy Matters IV av Denis Belomestny, Fabienne Comte, Valentine Genon-Catalot, Hiroki Masuda, Markus Reiss (häftad).

Köp båda 2 för 1808 kr

Kundrecensioner

Har du läst boken? Sätt ditt betyg »

Fler böcker av författarna

  • Levy Matters IV

    Denis Belomestny, Fabienne Comte, Valentine Genon-Catalot, Hiroki Masuda, Markus Reiss

    The aim of this volume is to provide an extensive account of the most recent advances in statistics for discretely observed Levy processes. These days, statistics for stochastic processes is a lively topic, driven by the needs of various fields of...

  • Robust Libor Modelling and Pricing of Derivative Products

    John Schoenmakers

    One of Riskbook.com's Best of 2005 - Top Ten Finance Books The Libor market model remains one of the most popular and advanced tools for modelling interest rates and interest rate derivatives, but finding a useful procedure for calibrating the mod...

Övrig information

Dr. John Schoenmakers (Berlin, Germany) is Deputy head of the Stochastic Algorithms and Nonparametric statistics research group at the Weierstrass Institute for Applied Analysis and Stochastics. His fields of interest include advanced modeling of equity and interest rate term structures, pricing and structuring of high dimensional callable derivatives, and general risk measures, stochastic modeling, Monte Carlo methods and many more. He has held the position of Visiting Professor at HU Berlin, and is on the editorial board of the Journal of Computational Finance, Monte Carlo Methods and its Applications, and International Journal of Portfolio Analysis and Management. Dr. Denis Belomestny (Duisburg, Germany) is Senior Researcher at Weierstrass Institute for Applied Analysis and Stochastics, where he works on the Statistical Data Analysis and Applied Mathematical Finance project. Previously, he was a researcher at the Institute for Applied Mathematics at Bonn University. His research interests include nonparametric statistics, stochastic processes and financial mathematics, and his research is published in a number of peer reviewed publications.

Innehållsförteckning

1. Introduction 2.- Basics of Monte Carlo methods 3.- Basics of standard optimal stopping, multiple stopping, and optimal control problem 4.- Dual representations for standard optimal stopping, multiple stopping, and optimal control problems. 5.- Primal algorithms for optimal stopping problems: regression algorithms, optimization algorithms, policy iteration. Extensions to multiple stopping, examples. 6.- Multilevel primal algorithms. 7.- Multilevel dual algorithms 8.- Convergence analysis of primal algorithms. 9.- Convergence analysis of dual algorithms. 10.- Consumption based approaches. 11.- Dimension reduction for primal algorithms. 12.- Variance reduction for dual algorithms. 13.- Conclusion.