Brownian motion and stochastic calculus /
Two of the most fundamental concepts in the theory of stochastic processes are the Markov property and the martingale property. * This book is written for readers who are acquainted with both of these ideas in the discrete-time setting, and who now wish to explore stochastic processes in their conti...
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| Other Authors: | |
| Format: | Book |
| Language: | English |
| Published: |
New York :
Springer-Verlag,
1988
New York : [1988], ©1988 New York : c1988 New York : ©1988 New York : 1988 New York : [1988] |
| Series: | Graduate texts in mathematics ;
113 Graduate texts in mathematics 113 Graduate texts in mathematics ; 113 Graduate texts in mathematics 113 |
| Subjects: |
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| 100 | 1 | |a Karatzas, Ioannis | |
| 245 | 1 | 0 | |a Brownian motion and stochastic calculus / |c Ioannis Karatzas, Steven E. Shreve |
| 260 | |a New York : |b Springer-Verlag, |c 1988 | ||
| 260 | |a New York : |b Springer-Verlag, |c [1988], ©1988 | ||
| 260 | |a New York : |b Springer-Verlag, |c c1988 | ||
| 260 | |a New York : |b Springer-Verlag, |c ©1988 | ||
| 260 | 0 | |a New York : |b Springer-Verlag, |c 1988 | |
| 264 | 1 | |a New York : |b Springer-Verlag, |c [1988] | |
| 264 | 4 | |c ©1988 | |
| 300 | |a xxiii, 470 p. : |b ill. ; |c 25 cm | ||
| 300 | |a xxiii, 470 p. ; |c 25 cm | ||
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| 440 | 0 | |a Graduate texts in mathematics ; |v 113 | |
| 440 | 0 | |a Graduate texts in mathematics |v 113 | |
| 490 | 1 | |a Graduate texts in mathematics ; |v 113 | |
| 490 | 1 | |a Graduate texts in mathematics |v 113 | |
| 500 | |a Includes index | ||
| 504 | |a Bibliography: p. [447]-458 | ||
| 504 | |a Bibliography: pages [447]-458 | ||
| 504 | |a Includes bibliographical references and index | ||
| 504 | |a Includes index | ||
| 505 | 0 | |a 1 Martingales, Stopping Times, and Filtrations -- 1.1. Stochastic Processes and ?-Fields -- 1.2. Stopping Times -- 1.3. Continuous-Time Martingales -- 1.4. The Doob-Meyer Decomposition -- 1.5. Continuous, Square-Integrable Martingales -- 1.6. Solutions to Selected Problems -- 1.7. Notes -- 2 Brownian Motion -- 2.1. Introduction -- 2.2. First Construction of Brownian Motion -- 2.3. Second Construction of Brownian Motion -- 2.4. The Space C [0, ?), Weak Convergence, and Wiener Measure -- 2.5. The Markov Property -- 2.6. The Strong Markov Property and the Reflection Principle -- 2.7. Brownian Filtrations -- 2.8. Computations Based on Passage Times -- 2.9. The Brownian Sample Paths -- 2.10. Solutions to Selected Problems -- 2.11. Notes -- 3 Stochastic Integration -- 3.1. Introduction -- 3.2. Construction of the Stochastic Integral -- 3.3. The Change-of-Variable Formula -- 3.4. Representations of Continuous Martingales in Terms of Brownian Motion -- 3.5. The Girsanov Theorem -- 3.6. Local Time and a Generalized Itô Rule for Brownian Motion -- 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 -- 4.3. The One-Dimensional Heat Equation -- 4.4. The Formulas of Feynman and Kac -- 4.5. Solutions to selected problems -- 4.6. Notes -- 5 Stochastic Differential Equations -- 5.1. Introduction -- 5.2. Strong Solutions -- 5.3. Weak Solutions -- 5.4. The Martingale Problem of Stroock and Varadhan -- 5.5. A Study of the One-Dimensional Case -- 5.6. Linear Equations -- 5.7. Connections with Partial Differential Equations -- 5.8. Applications to Economics -- 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 -- 6.3. Two Independent Reflected Brownian Motions -- 6.4. Elastic Brownian Motion -- 6.5. An Application: Transition Probabilities of Brownian Motion with Two-Valued Drift -- 6.6. Solutions to Selected Problems -- 6.7. Notes | |
| 520 | |a Two of the most fundamental concepts in the theory of stochastic processes are the Markov property and the martingale property. * This book is written for readers who are acquainted with both of these ideas in the discrete-time setting, and who now wish to explore stochastic processes in their continuous time context. It has been our goal to write a systematic and thorough exposi tion of this subject, leading in many instances to the frontiers of knowledge. At the same time, we have endeavored to keep the mathematical prerequisites as low as possible, namely, knowledge of measure-theoretic probability and some familiarity with discrete-time processes. The vehicle we have chosen for this task is Brownian motion, which we present as the canonical example of both a Markov process and a martingale. We support this point of view by showing how, by means of stochastic integration and random time change, all continuous-path martingales and a multitude of continuous-path Markov processes can be represented in terms of Brownian motion. This approach forces us to leave aside those processes which do not have continuous paths. Thus, the Poisson process is not a primary object of study, although it is developed in Chapter 1 to be used as a tool when we later study passage times and local time of Brownian motion | ||
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| 650 | 0 | |a Stochastic analysis | |
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| 650 | 7 | |a Stochastic analysis |2 fast | |
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