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Stochastic Integration Theory

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ISBN-10: 0199215251

ISBN-13: 9780199215256

Edition: 2007

Authors: Peter Medvegyev

List price: $140.00
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This graduate level text covers the theory of stochastic integration, an important area of mathematics that has a wide range of applications, including financial mathematics and signal processing. Aimed at graduate students in mathematics, statistics, probability, mathematical finance, and economics, the book not only covers the theory of the stochastic integral in great depth but also presents the associated theory (martingales, Levy processes) and important examples (Brownian motion, Poisson process).
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Book details

List price: $140.00
Copyright year: 2007
Publisher: Oxford University Press, Incorporated
Publication date: 9/6/2007
Binding: Hardcover
Pages: 632
Size: 6.42" wide x 9.45" long x 1.50" tall
Weight: 2.332
Language: English

Preface
Stochastic processes
Random functions
Trajectories of stochastic processes
Jumps of stochastic processes
When are stochastic processes equal?
Measurability of Stochastic Processes
Filtration, adapted, and progressively measurable processes
Stopping times
Stopped variables, [sigma]-algebras, and truncated processes
Predictable processes
Martingales
Doob's inequalities
The energy equality
The quadratic variation of discrete time martingales
The downcrossings inequality
Regularization of martingales
The Optional Sampling Theorem
Application: elementary properties of Levy processes
Application: the first passage times of the Wiener processes
Some remarks on the usual assumptions
Localization
Stability under truncation
Local martingales
Convergence of local martingales: uniform convergence on compacts in probability
Locally bounded processes
Stochastic Integration with Locally Square-Integrable Martingales
The Ito-Stieltjes Integrals
Ito-Stieltjes integrals when the integrators have finite variation
Ito-Stieltjes integrals when the integrators are locally square-integrable martingales
Ito-Stieltjes integrals when the integrators are semimartingales
Properties of the Ito-Stieltjes integral
The integral process
Integration by parts and the existence of the quadratic variation
The Kunita-Watanabe inequality
The Quadratic Variation of Continuous Local Martingales
Integration when Integrators are Continuous Semimartingales
The space of square-integrable continuous local martingales
Integration with respect to continuous local martingales
Integration with respect to semimartingales
The Dominated Convergence Theorem for stochastic integrals
Stochastic integration and the Ito-Stieltjes integral
Integration when Integrators are Locally Square-Integrable Martingales
The quadratic variation of locally square-integrable martingales
Integration when the integrators are locally square-integrable martingales
Stochastic integration when the integrators are semimartingales
The Structure of Local Martingales
Predictable Projection
Predictable stopping times
Decomposition of thin sets
The extended conditional expectation
Definition of the predictable projection
The uniqueness of the predictable projection, the predictable section theorem
Properties of the predictable projection
Predictable projection of local martingales
Existence of the predictable projection
Predictable Compensators
Predictable Radon-Nikodym Theorem
Predictable Compensator of locally integrable processes
Properties of the Predictable Compensator
The Fundamental Theorem of Local Martingales
Quadratic Variation
General Theory of Stochastic Integration
Purely Discontinuous Local Martingales
Orthogonality of local martingales
Decomposition of local martingales
Decomposition of semimartingales
Purely Discontinuous Local Martingales and Compensated Jumps
Construction of purely discontinuous local martingales
Quadratic variation of purely discontinuous local martingales
Stochastic Integration With Respect To Local Martingales
Definition of stochastic integration
Properties of stochastic integration
Stochastic Integration With Respect To Semimartingales
Integration with respect to special semimartingales
Linearity of the stochastic integral
The associativity rule
Change of measure
The Proof of Davis' Inequality
Discrete-time Davis' inequality
Burkholder's inequality
Some Other Theorems
The Doob-Meyer Decomposition
The proof of the theorem
Dellacherie's formulas and the natural processes
The sub- super- and the quasi-martingales are semimartingales
Semimartingales as Good Integrators
Integration of Adapted Product Measurable Processes
Theorem of Fubini for Stochastic Integrals
Martingale Representation
Ito's Formula
Ito's Formula for Continuous Semimartingales
Some Applications of the Formula
Zeros of Wiener processes
Continuous Levy processes
Levy's characterization of Wiener processes
Integral representation theorems for Wiener processes
Bessel processes
Change of Measure for Continuous Semimartingales
Locally absolutely continuous change of measure
Semimartingales and change of measure
Change of measure for continuous semimartingales
Girsanov's formula for Wiener processes
Kazamaki-Novikov criteria
Ito's Formula for Non-Continuous Semimartingales
Ito's formula for processes with finite variation
The proof of Ito's formula
Exponential semimartingales
Ito's Formula For Convex Functions
Derivative of convex functions
Definition of local times
Meyer-Ito formula
Local times of continuous semimartingales
Local time of Wiener processes
Ray-Knight theorem
Theorem of Dvoretzky Erdos and Kakutani
Processes with Independent Increments
Levy processes
Poisson processes
Compound Poisson processes generated by the jumps
Spectral measure of Levy processes
Decomposition of Levy processes
Levy-Khintchine formula for Levy processes
Construction of Levy processes
Uniqueness of the representation
Predictable Compensators of Random Measures
Measurable random measures
Existence of predictable compensator
Characteristics of Semimartingales
Levy-Khintchine Formula for Semimartingales with Independent Increments
Examples: probability of jumps of processes with independent increments
Predictable cumulants
Semimartingales with independent increments
Characteristics of semimartingales with independent increments
The proof of the formula
Decomposition of Processes with Independent Increments
Appendix
Results from Measure Theory
The Monotone Class Theorem
Projection and the Measurable Selection Theorems
Cramer's Theorem
Interpretation of Stopped [sigma]-algebras
Wiener Processes
Basic Properties
Existence of Wiener Processes
Quadratic Variation of Wiener Processes
Poisson processes
Notes and Comments
References
Index