Introduction to Stochastic Integration

“Introduction to Stochastic Integration” by Kai Lai Chung is a comprehensive resource published by Springer New York on November 10, 2013. This second edition, spanning 276 pages, serves as a highly readable introduction to stochastic integration and stochastic differential equations, making it suitable for graduate courses in stochastic calculus following a course in probability. The book presents the basic theory alongside practical applications, defining the stochastic integral for predictable integrands and local martingales, and developing the change of variable formula for continuous martingales.
Readers will find a thorough exploration of topics such as Brownian motion, Hermite polynomials of martingales, and the Feynman–Kac functional, as well as discussions on local time and reflected Brownian motion. The second edition includes new content on the Cameron–Martin–Girsanov transformation and introduces stochastic differential equations, complemented by numerous exercises for classroom use. This text is designed to be a valuable resource for mathematicians, statisticians, economists, and engineers interested in the modern tools of stochastic analysis.
Official synopsis Publisher
A highly readable introduction to stochastic integration and stochastic differential equations, this book combines developments of the basic theory with applications. It is written in a style suitable for the text of a graduate course in stochastic calculus, following a course in probability.
Using the modern approach, the stochastic integral is defined for predictable integrands and local martingales; then It’s change of variable formula is developed for continuous martingales. Applications include a characterization of Brownian motion, Hermite polynomials of martingales, the Feynman–Kac functional and the Schrödinger equation. For Brownian motion, the topics of local time, reflected Brownian motion, and time change are discussed.
New to the second edition are a discussion of the Cameron–Martin–Girsanov transformation and a final chapter which provides an introduction to stochastic differential equations, as well as many exercises for classroom use.
This book will be a valuable resource to all mathematicians, statisticians, economists, and engineers employing the modern tools of stochastic analysis.
The text also proves that stochastic integration has made an important impact on mathematical progress over the last decades and that stochastic calculus has become one of the most powerful tools in modern probability theory.
—Journal of the American Statistical Association
An attractive text…written in [a] lean and precise style…eminently readable. Especially pleasant are the care and attention devoted to details… A very fine book.
—Mathematical Reviews
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