Stochastic Methods in Asset Pricing (MIT Press)
| Author | : | |
| Rating | : | 4.34 (822 Votes) |
| Asin | : | 026203655X |
| Format Type | : | paperback |
| Number of Pages | : | 632 Pages |
| Publish Date | : | 2014-08-21 |
| Language | : | English |
DESCRIPTION:
(Paul Glasserman, Jack R. Hines Jr. (Jessica Wachter, Richard B. (Viktor Todorov, Harold H. Professor of Risk Management, Kellogg School of Management, Northwestern University)This beautiful book is for students and established researchers seeking deeper knowledge of the mathematics behind theories of asset pricing. In this ambitious book, the author guides a dedicated reader from elementary probability to the advanced stochastic analysis of modern mathematical finance, with rewarding excursions into topics seldom
This book presents a self-contained, comprehensive, and yet concise and condensed overview of the theory and methods of probability, integration, stochastic processes, optimal control, and their connections to the principles of asset pricing. It covers concrete option pricing models (including stochastic volatility, exchange options, and the exercise of American options), Merton's investment--consumption problem, and several other applications. The book includes more than 450 exercises (with detailed hints). For asset pricing, the book begins with a brief overview of risk preferences and general equilibrium in incomplete finite endowment economies, followed by the classical asset pricing setup in continuous time. The book is broader in scope than other introductory-level graduate texts on the subject, requires fewer prerequisites, and covers the relevant material at greater depth, mainly without rigorous technical proofs. The book brings to an introductory level certain concepts and topics that are usually found in advanced research monographs on stochastic processes and asset pricing, and it attempts to establish greater clarity on the connections between these two fields. The book begins with measure-theoretic probability and integration, and then develops the classical tools of stochastic c
. Andrew Lyasoff is affiliated with the Mathematical Finance Program at Boston University's Questrom School of Business