A Course of Stochastic Analysis

by Alexander Melnikov

Overview

The main subject of the book is stochastic analysis and its various applications to mathematical finance and statistics of random processes. The main purpose of the book is to present, in a short and sufficiently self-contained form, the methods and results of the contemporary theory of stochastic analysis and to show how these methods and results work in mathematical finance and statistics of random processes. The book can be considered as a textbook for both senior undergraduate and graduate courses on this subject. The book can be helpful for undergraduate and graduate students, instructors and specialists on stochastic analysis and its applications.

Product Details

ISBN-13:	9783031253263
Publisher:	Springer-Verlag New York, LLC
Publication date:	04/02/2023
Series:	CMS/CAIMS Books in Mathematics , #6
Sold by:	Barnes & Noble
Format:	eBook
File size:	24 MB
Note:	This product may take a few minutes to download.

About the Author

Alexander Melnikov is a Professor at the University of Alberta working in stochastic analysis and its applications in finance, statistics and insurance. He is the author of eight books and over one hundred papers in leading academic journals and venues. He is a Fellow of the Russian Academy of Natural Sciences, a recipient of the Leontiev medal of this academy and the McCalla Professorship of the University of Alberta. In addition to his academic engagements, he held several senior positions in business and professional organizations: Chief-Scientist at Risk-Invest Deutschland (Frankfurt), Vice-President of the Russian Society of Actuaries, Deputy Director at the Center for Actuarial and Financial Studies (Moscow), Senior Research Consultant at the Model Capital Management (Boston).

Table of Contents

1 Probabilistic Foundations.- 2 Random variables and their quantitative characteristics.- 3 Expectations and convergence of sequences of random variables.- 4 Weak convergence of sequences of random variables.- 5 Absolute continuity of probability measures and conditional expectations.- 6 Discrete time stochastic analysis: basic results.- 7 Discrete time stochastic analysis: further results and applications.- 8 Elements of classical theory of stochastic processes.- 9 Stochastic differential equations, diffusion processes and their applications.- 10 General theory of stochastic processes under ”usual conditions".- 11 General theory of stochastic processes in applications.- 12 Supplementary problems.- References.- Index.

Editorial Reviews

The book can be considered as a textbook for both senior undergraduate and graduate courses on shastic analysis. It can be helpful also for instructors and specialists in this area.” (Pavel Stoynov, zbMATH 1543.60001, 2024)

From the Publisher