American-type options. Volume 1, Stochastic approximation methods

American-type options. Volume 1, Stochastic approximation methods

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Bibliographic Details
Main Author: Silʹvestrov, D. S. (Dmitriĭ Sergeevich)
Corporate Author: Ebooks Corporation
Format: Electronic eBook
Language:English
Published: Berlin ; Boston : De Gruyter, c2014.
Series:De Gruyter studies in mathematics ; 56.
Subjects:
Options (Finance) > Mathematical models.
Stochastic approximation.
Markov processes.
Online Access:Connect to this title online (unlimited simultaneous users allowed; 325 uses per year)

MARC

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050 4 |a HG6024.A3  |b S55 2014 vol. 1 
082 0 4 |a 332.64/53  |2 23 
100 1 |a Silʹvestrov, D. S.  |q (Dmitriĭ Sergeevich) 
245 1 0 |a American-type options.  |n Volume 1,  |p Stochastic approximation methods  |h [electronic resource] /  |c Dmitrii S. Silverstrov. 
246 3 0 |a Stochastic approximation methods 
260 |a Berlin ;  |a Boston :  |b De Gruyter,  |c c2014. 
300 |a 1 online resource. 
490 1 |a De Gruyter studies in mathematics ;  |v v. 56 
500 |a Print version cataloged as a monographic set by Library of Congress. 
533 |a Electronic reproduction.  |b Perth, W.A.  |n Available via World Wide Web. 
588 |a Description based on online resource; title from digital title page (viewed on Jan. 15, 2014). 
505 0 0 |a Machine generated contents note:   |g 1.  |t Multivariate modulated Markov log-price processes (LPP) --   |g 1.1.  |t Markov LPP --   |g 1.2.  |t LPP represented by random walks --   |g 1.3.  |t Autoregressive LPP --   |g 1.4.  |t Autoregressive stochastic volatility LPP --   |g 2.  |t American-type options --   |g 2.1.  |t American-type options --   |g 2.2.  |t Pay-off functions --   |g 2.3.  |t Reward and log-reward functions --   |g 2.4.  |t Optimal stopping times --   |g 2.5.  |t American-type knockout options --   |g 3.  |t Backward recurrence reward algorithms --   |g 3.1.  |t Binomial tree reward algorithms --   |g 3.2.  |t Trinomial tree reward algorithms --   |g 3.3.  |t Random walk reward algorithms --   |g 3.4.  |t Markov chain reward algorithms --   |g 4.  |t Upper bounds for option rewards --   |g 4.1.  |t Markov LPP with bounded characteristics --   |g 4.2.  |t LPP represented by random walks --   |g 4.3.  |t Markov LPP with unbounded characteristics --   |g 4.4.  |t Univariate Markov Gaussian LPP --   |g 4.5.  |t Multivariate modulated Markov Gaussian LPP --   |g 5.  |t Convergence of option rewards -- I --   |g 5.1.  |t Asymptotically uniform upper bounds for rewards -- I --   |g 5.2.  |t Modulated Markov LPP with bounded characteristics --   |g 5.3.  |t LPP represented by modulated random walks --   |g 6.  |t Convergence of option rewards -- II --   |g 6.1.  |t Asymptotically uniform upper bounds for rewards -- II --   |g 6.2.  |t Univariate modulated LPP with unbounded characteristics --   |g 6.3.  |t Asymptotically uniform upper bounds for rewards -- III --   |g 6.4.  |t Multivariate modulated LPP with unbounded characteristics --   |g 6.5.  |t Conditions of convergence for Markov price processes --   |g 7.  |t Space-skeleton reward approximations --   |g 7.1.  |t Atomic approximation models --   |g 7.2.  |t Univariate Markov LPP with bounded characteristics --   |g 7.3.  |t Multivariate Markov LPP with bounded characteristics --   |g 7.4.  |t LPP represented by multivariate modulated random walks --   |g 7.5.  |t Multivariate Markov LPP with unbounded characteristics --   |g 8.  |t Convergence of rewards for Markov Gaussian LPP --   |g 8.1.  |t Univariate Markov Gaussian LPP --   |g 8.2.  |t Multivariate modulated Markov Gaussian LPP --   |g 8.3.  |t Markov Gaussian LPP with estimated characteristics --   |g 8.4.  |t Skeleton reward approximations for Markov Gaussian LPP --   |g 8.5.  |t LPP represented by Gaussian random walks --   |g 9.  |t Tree-type approximations for Markov Gaussian LPP --   |g 9.1.  |t Univariate binomial tree approximations --   |g 9.2.  |t Multivariate binomial tree approximations --   |g 9.3.  |t Multivariate trinomial tree approximations --   |g 9.4.  |t Inhomogeneous in space binomial approximations --   |g 9.5.  |t Inhomogeneous in time and space trinomial approximations --   |g 10.  |t Convergence of tree-type reward approximations --   |g 10.1.  |t Univariate binomial tree approximation models --   |g 10.2.  |t Multivariate homogeneous in space tree models --   |g 10.3.  |t Univariate inhomogeneous in space tree models --   |g 10.4.  |t Multivariate inhomogeneous in space tree models. 
650 0 |a Options (Finance)  |x Mathematical models. 
650 0 |a Stochastic approximation. 
650 0 |a Markov processes. 
710 2 |a Ebooks Corporation 
776 0 8 |i Print version:  |a Silvestrov, Dmitrii S  |t American-Type Options : Stochastic Approximation Methods, Volume 1  |d Berlin : De Gruyter,c2013  |z 9783110329674 
830 0 |a De Gruyter studies in mathematics ;  |v 56. 
856 4 0 |u https://ebookcentral.proquest.com/lib/santaclara/detail.action?docID=1575440  |z Connect to this title online (unlimited simultaneous users allowed; 325 uses per year)  |t 1 
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