Muutke küpsiste eelistusi

E-raamat: Stochastic Analysis in Discrete and Continuous Settings: With Normal Martingales

  • Formaat: PDF+DRM
  • Sari: Lecture Notes in Mathematics 1982
  • Ilmumisaeg: 14-Jul-2009
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Keel: eng
  • ISBN-13: 9783642023804
  • Formaat - PDF+DRM
  • Hind: 55,56 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
  • Formaat: PDF+DRM
  • Sari: Lecture Notes in Mathematics 1982
  • Ilmumisaeg: 14-Jul-2009
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • Keel: eng
  • ISBN-13: 9783642023804

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

This unified presentation of stochastic analysis for continuous and discontinuous stochastic processes in both discrete and continuous time is mostly self-contained and accessible to readers who have already received a basic training in probability.



This monograph is an introduction to some aspects of stochastic analysis in the framework of normal martingales, in both discrete and continuous time. The text is mostly self-contained, except for Section 5.7 that requires some background in geometry, and should be accessible to graduate students and researchers having already received a basic training in probability. Prereq- sites are mostly limited to a knowledge of measure theory and probability, namely -algebras,expectations,andconditionalexpectations.Ashortint- duction to stochastic calculus for continuous and jump processes is given in Chapter 2 using normal martingales, whose predictable quadratic variation is the Lebesgue measure. There already exists several books devoted to stochastic analysis for c- tinuous di usion processes on Gaussian and Wiener spaces, cf. e.g. [ 51], [ 63], [ 65], [ 72], [ 83], [ 84], [ 92], [ 128], [ 134], [ 143], [ 146], [ 147]. The particular f- ture of this text is to simultaneously consider continuous processes and jump processes in the uni ed framework of normal martingales.

Arvustused

From the reviews:

The author presents several aspects of stochastic analysis for discrete and continuous-time normal martingales. variety of operators on the Poisson space is an highlight of this book. It is finally worth mentioning that this volume of the Lecture Notes in Mathematics includes many interesting applications and that the various notions, properties and proofs are clear and detailed. (A. Réveillac, Zentralblatt MATH, Vol. 1185, 2010)

The book under review has the original feature of giving a unified treatment to all normal martingales. The book is quite accessible to beginners. its main goal is providing advanced researchers with a study of stochastic analysis in both discrete and continuous time and with a simultaneous treatment of both continuous and jump processes. (Dominique Lépingle, Mathematical Reviews, Issue 2011 j)

Introduction 1(6)
The Discrete Time Case
7(52)
Normal Martingales
7(1)
Stochastic Integrals
8(3)
Multiple Stochastic Integrals
11(2)
Structure Equations
13(2)
Chaos Representation
15(3)
Gradient Operator
18(4)
Clark Formula and Predictable Representation
22(2)
Divergence Operator
24(4)
Ornstein-Uhlenbeck Semi-Group and Process
28(4)
Covariance Identities
32(4)
Deviation Inequalities
36(6)
Logarithmic Sobolev Inequalities
42(7)
Change of Variable Formula
49(4)
Option Hedging
53(5)
Notes and References
58(1)
Continuous Time Normal Martingales
59(54)
Normal Martingales
59(1)
Brownian Motion
60(3)
Compensated Poisson Martingale
63(8)
Compound Poisson Martingale
71(3)
Stochastic Integrals
74(10)
Predictable Representation Property
84(2)
Multiple Stochastic Integrals
86(3)
Chaos Representation Property
89(1)
Quadratic Variation
90(3)
Structure Equations
93(3)
Product Formula for Stochastic Integrals
96(6)
Ito Formula
102(5)
Exponential Vectors
107(2)
Vector-Valued Case
109(2)
Notes and References
111(2)
Gradient and Divergence Operators
113(18)
Definition and Closability
113(1)
Clark Formula and Predictable Representation
114(5)
Divergence and Stochastic Integrals
119(2)
Covariance Identities
121(2)
Logarithmic Sobolev Inequalities
123(2)
Deviation Inequalities
125(2)
Markovian Representation
127(3)
Notes and References
130(1)
Annihilation and Creation Operators
131(30)
Duality Relation
131(3)
Annihilation Operator
134(4)
Creation Operator
138(6)
Ornstein-Uhlenbeck Semi-Group
144(2)
Deterministic Structure Equations
146(5)
Exponential Vectors
151(3)
Deviation Inequalities
154(4)
Derivation of Fock Kernels
158(2)
Notes and References
160(1)
Analysis on the Wiener Space
161(34)
Multiple Wiener Integrals
161(5)
Gradient and Divergence Operators
166(5)
Ornstein-Uhlenbeck Semi-Group
171(2)
Covariance Identities and Inequalities
173(4)
Moment Identities for Skorohod Integrals
177(3)
Differential Calculus on Random Morphisms
180(6)
Riemannian Brownian Motion
186(6)
Time Changes on Brownian Motion
192(2)
Notes and References
194(1)
Analysis on the Poisson Space
195(52)
Poisson Random Measures
195(8)
Multiple Poisson Stochastic Integrals
203(9)
Chaos Representation Property
212(6)
Finite Difference Gradient
218(8)
Divergence Operator
226(5)
Characterization of Poisson Measures
231(3)
Clark Formula and Levy Processes
234(3)
Covariance Identities
237(4)
Deviation Inequalities
241(4)
Notes and References
245(2)
Local Gradients on the Poisson Space
247(34)
Intrinsic Gradient on Configuration Spaces
247(8)
Damped Gradient on the Half Line
255(8)
Damped Gradient on a Compact Interval
263(4)
Chaos Expansions
267(3)
Covariance Identities and Deviation Inequalities
270(2)
Some Geometric Aspects of Poisson Analysis
272(5)
Chaos Interpretation of Time Changes
277(3)
Notes and References
280(1)
Option Hedging in Continuous Time
281(14)
Market Model
281(3)
Hedging by the Clark Formula
284(4)
Black-Scholes PDE
288(2)
Asian Options and Deterministic Structure
290(3)
Notes and References
293(2)
Appendix
295(6)
Measurability
295(1)
Gaussian Random Variables
295(1)
Conditional Expectation
296(1)
Martingales in Discrete Time
296(1)
Martingales in Continuous Time
297(1)
Markov Processes
298(1)
Tensor Products of L2 Spaces
299(1)
Closability of Linear Operators
300(1)
References 301(8)
Index 309