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More About This Textbook
Overview
A graduatecourse text, written for readers familiar with measuretheoretic probability and discretetime processes, wishing to explore shastic processes in continuous time. The vehicle chosen for this exposition is Brownian motion, which is presented as the canonical example of both a martingale and a Markov process with continuous paths. In this context, the theory of shastic integration and shastic calculus is developed, illustrated by results concerning representations of martingales and change of measure on Wiener space, which in turn permit a presentation of recent advances in financial economics. The book contains a detailed discussion of weak and strong solutions of shastic differential equations and a study of local time for semimartingales, with special emphasis on the theory of Brownian local time. The whole is backed by a large number of problems and exercises.
Editorial Reviews
From the Publisher
Second EditionI. Karatzas and S.E. Shreve
Brownian Motion and Shastic Calculus
"A valuable book for every graduate student studying shastic process, and for those who are interested in pure and applied probability. The authors have done a good job."—MATHEMATICAL REVIEWS
Booknews
For readers familiar with measuretheoretic probability and discrete time processes, who wish to explore stochastic processes in continuous time. Annotation c. Book News, Inc., Portland, OR (booknews.com)Product Details
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Table of Contents
1 Martingales, Stopping Times, and Filtrations. 1.1. Shastic Processes and—Fields. 1.2. Stopping Times. 1.3. ContinuousTime Martingales. A. Fundamental inequalities. B. Convergence results. C. The optional sampling theorem. 1.4. The Doob—Meyer Decomposition. 1.5. Continuous, SquareIntegrable Martingales. 1.6. Solutions to Selected Problems. 1.7. Notes. 2 Brownian Motion. 2.1. Introduction. 2.2. First Construction of Brownian Motion. A. The consistency theorem. B. The Kolmogorov—?entsov theorem. 2.3. Second Construction of Brownian Motion. 2.4. The SpaceC[0,—), Weak Convergence, and Wiener Measure. A. Weak convergence. B. Tightness. C. Convergence of finitedimensional distributions. D. The invariance principle and the Wiener measure. 2.5. The Markov Property. A. Brownian motion in several dimensions. B. Markov processes and Markov families. C. Equivalent formulations of the Markov property. 2.6. The Strong Markov Property and the Reflection Principle. A. The reflection principle. B. Strong Markov processes and families. C. The strong Markov property for Brownian motion. 2.7. Brownian Filtrations. A. Rightcontinuity of the augmented filtration for a strong Markov process. B. A “universal” filtration. C. The Blumenthal zeroone law. 2.8. Computations Based on Passage Times. A. Brownian motion and its running maximum. B. Brownian motion on a halfline. C. Brownian motion on a finite interval. D. Distributions involving last exit times. 2.9. The Brownian Sample Paths. A. Elementary properties. B. The zero set and the quadratic variation. C. Local maxima and points of increase. D. Nowhere differentiability. E. Law of the iterated logarithm. F. Modulus of continuity. 2.10. Solutions to Selected Problems. 2.11. Notes. 3 Shastic Integration. 3.1. Introduction. 3.2. Construction of the Shastic Integral. A. Simple processes and approximations. B. Construction and elementary properties of the integral. C. A characterization of the integral. D. Integration with respect to continuous, local martingales. 3.3. The ChangeofVariable Formula. A. The Itô rule. B. Martingale characterization of Brownian motion. C. Bessel processes, questions of recurrence. D. Martingale moment inequalities. E. Supplementary exercises. 3.4. Representations of Continuous Martingales in Terms of Brownian Motion. A. Continuous local martingales as shastic integrals with respect to Brownian motion. B. Continuous local martingales as timechanged Brownian motions. C. A theorem of F. B. Knight. D. Brownian martingales as shastic integrals. E. Brownian functionals as shastic integrals. 3.5. The Girsanov Theorem. A. The basic result. B. Proof and ramifications. C. Brownian motion with drift. D. The Novikov condition. 3.6. Local Time and a Generalized Itô Rule for Brownian Motion. A. Definition of local time and the Tanaka formula. B. The Trotter existence theorem. C. Reflected Brownian motion and the Skorohod equation. D. A generalized Itô rule for convex functions. E. The Engelbert—Schmidt zeroone law. 3.7. Local Time for Continuous Semimartingales. 3.8. Solutions to Selected Problems. 3.9. Notes. 4 Brownian Motion and Partial Differential Equations. 4.1. Introduction. 4.2. Harmonic Functions and the Dirichlet Problem. A. The meanvalue property. B. The Dirichlet problem. C. Conditions for regularity. D. Integral formulas of Poisson. E. Supplementary exercises. 4.3. The OneDimensional Heat Equation. A. The Tychonoff uniqueness theorem. B. Nonnegative solutions of the heat equation. C. Boundary crossing probabilities for Brownian motion. D. Mixed initial/boundary value problems. 4.4. The Formulas of Feynman and Kac. A. The multidimensional formula. B. The onedimensional formula. 4.5. Solutions to selected problems. 4.6. Notes. 5 Shastic Differential Equations. 5.1. Introduction. 5.2. Strong Solutions. A. Definitions. B. The Itô theory. C. Comparison results and other refinements. D. Approximations of shastic differential equations. E. Supplementary exercises. 5.3. Weak Solutions. A. Two notions of uniqueness. B. Weak solutions by means of the Girsanov theorem. C. A digression on regular conditional probabilities. D. Results of Yamada and Watanabe on weak and strong solutions. 5.4. The Martingale Problem of Stroock and Varadhan. A. Some fundamental martingales. B. Weak solutions and martingale problems. C. Wellposedness and the strong Markov property. D. Questions of existence. E. Questions of uniqueness. F. Supplementary exercises. 5.5. A Study of the OneDimensional Case. A. The method of time change. B. The method of removal of drift. C. Feller’s test for explosions. D. Supplementary exercises. 5.6. Linear Equations. A. Gauss—Markov processes. B. Brownian bridge. C. The general, onedimensional, linear equation. D. Supplementary exercises. 5.7. Connections with Partial Differential Equations. A. The Dirichlet problem. B. The Cauchy problem and a Feynman—Kac representation. C. Supplementary exercises. 5.8. Applications to Economics. A. Portfolio and consumption processes. B. Option pricing. C. Optimal consumption and investment (general theory). D. Optimal consumption and investment (constant coefficients). 5.9. Solutions to Selected Problems. 5.10. Notes. 6 P. Lévy’s Theory of Brownian Local Time. 6.1. Introduction. 6.2. Alternate Representations of Brownian Local Time. A. The process of passage times. B. Poisson random measures. C. Subordinators. D. The process of passage times revisited. E. The excursion and downcrossing representations of local time. 6.3. Two Independent Reflected Brownian Motions. A. The positive and negative parts of a Brownian motion. B. The first formula of D. Williams. C. The joint density of (W(t), L(t),—
+(t)). 6.4. Elastic Brownian Motion. A. The Feynman—Kac formulas for elastic Brownian motion. B. The Ray—Knight description of local time. C. The second formula of D. Williams. 6.5. An Application: Transition Probabilities of Brownian Motion with TwoValued Drift. 6.6. Solutions to Selected Problems. 6.7. Notes.