Brownian Motion and Stochastic Calculus

  • Ioannis Karatzas
  • Steven E. Shreve

Part of the Graduate Texts in Mathematics book series (GTM, volume 113)

Table of contents

  1. Front Matter
    Pages i-xxiii
  2. Ioannis Karatzas, Steven E. Shreve
    Pages 1-46
  3. Ioannis Karatzas, Steven E. Shreve
    Pages 47-127
  4. Ioannis Karatzas, Steven E. Shreve
    Pages 128-238
  5. Ioannis Karatzas, Steven E. Shreve
    Pages 239-280
  6. Ioannis Karatzas, Steven E. Shreve
    Pages 281-398
  7. Ioannis Karatzas, Steven E. Shreve
    Pages 399-446
  8. Back Matter
    Pages 447-470

About this book


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.


Brownian motion Girsanov theorem Markov process Markov property Martingal Martingale Semimartingale Stochastic calculus continuous-time stochastic process differential equation filtration local time reflected Brownian motion stochastic differential equation stochastic processes

Authors and affiliations

  • Ioannis Karatzas
    • 1
  • Steven E. Shreve
    • 2
  1. 1.Department of StatisticsColumbia UniversityNew YorkUSA
  2. 2.Department of MathematicsCarnegie Mellon UniversityPittsburghUSA

Bibliographic information

  • DOI
  • Copyright Information Springer-Verlag New York 1988
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4684-0304-6
  • Online ISBN 978-1-4684-0302-2
  • Series Print ISSN 0072-5285
  • Buy this book on publisher's site
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