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American-Type Options : Stochastic Approximation Methods. Volume 1, American-Type Options ; Stochastic Approximation Methods, Volume 1 / Dmitrii S. Silvestrov.

By: Material type: TextTextSeries: De Gruyter Studies in Mathematics ; 56Publisher: Berlin ; Boston : De Gruyter, [2013]Copyright date: ©2014Description: 1 online resource (509 p.)Content type:
Media type:
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ISBN:
  • 9783110329674
  • 9783110329827
Subject(s): Other classification:
  • online - DeGruyter
Online resources: Available additional physical forms:
  • Issued also in print.
Contents:
Frontmatter -- Preface -- Contents -- 1. Multivariate modulated Markov log-price processes (LPP) -- 2. American-type options -- 3. Backward recurrence reward algorithms -- 4. Upper bounds for option rewards -- 5. Convergence of option rewards – I -- 6. Convergence of option rewards – II -- 7. Space-skeleton reward approximations -- 8. Convergence of rewards for Markov Gaussian LPP -- 9. Tree-type approximations for Markov Gaussian LPP -- 10. Convergence of tree-type reward approximations -- Bibliographical Remarks -- Bibliography -- Index -- Backmatter
Summary: The book gives a systematical presentation of stochastic approximation methods for models of American-type options with general pay-off functions for discrete time Markov price processes. Advanced methods combining backward recurrence algorithms for computing of option rewards and general results on convergence of stochastic space skeleton and tree approximations for option rewards are applied to a variety of models of multivariate modulated Markov price processes. The principal novelty of presented results is based on consideration of multivariate modulated Markov price processes and general pay-off functions, which can depend not only on price but also an additional stochastic modulating index component, and use of minimal conditions of smoothness for transition probabilities and pay-off functions, compactness conditions for log-price processes and rate of growth conditions for pay-off functions. The book also contains an extended bibliography of works in the area. This book is the first volume of the comprehensive two volumes monograph. The second volume will present results on structural studies of optimal stopping domains, Monte Carlo based approximation reward algorithms, and convergence of American-type options for autoregressive and continuous time models, as well as results of the corresponding experimental studies.
Holdings
Item type Current library Call number URL Status Notes Barcode
eBook eBook Biblioteca "Angelicum" Pont. Univ. S.Tommaso d'Aquino Nuvola online online - DeGruyter (Browse shelf(Opens below)) Online access Not for loan (Accesso limitato) Accesso per gli utenti autorizzati / Access for authorized users (dgr)9783110329827

Frontmatter -- Preface -- Contents -- 1. Multivariate modulated Markov log-price processes (LPP) -- 2. American-type options -- 3. Backward recurrence reward algorithms -- 4. Upper bounds for option rewards -- 5. Convergence of option rewards – I -- 6. Convergence of option rewards – II -- 7. Space-skeleton reward approximations -- 8. Convergence of rewards for Markov Gaussian LPP -- 9. Tree-type approximations for Markov Gaussian LPP -- 10. Convergence of tree-type reward approximations -- Bibliographical Remarks -- Bibliography -- Index -- Backmatter

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http://purl.org/coar/access_right/c_16ec

The book gives a systematical presentation of stochastic approximation methods for models of American-type options with general pay-off functions for discrete time Markov price processes. Advanced methods combining backward recurrence algorithms for computing of option rewards and general results on convergence of stochastic space skeleton and tree approximations for option rewards are applied to a variety of models of multivariate modulated Markov price processes. The principal novelty of presented results is based on consideration of multivariate modulated Markov price processes and general pay-off functions, which can depend not only on price but also an additional stochastic modulating index component, and use of minimal conditions of smoothness for transition probabilities and pay-off functions, compactness conditions for log-price processes and rate of growth conditions for pay-off functions. The book also contains an extended bibliography of works in the area. This book is the first volume of the comprehensive two volumes monograph. The second volume will present results on structural studies of optimal stopping domains, Monte Carlo based approximation reward algorithms, and convergence of American-type options for autoregressive and continuous time models, as well as results of the corresponding experimental studies.

Issued also in print.

Mode of access: Internet via World Wide Web.

In English.

Description based on online resource; title from PDF title page (publisher's Web site, viewed 28. Feb 2023)