Foundations of Quantitative Finance Book IV: Distribution Functions and Expectations

Every finance professional wants and needs a competitive edge. A firm foundation in advanced mathematics can translate into dramatic advantages to professionals willing to obtain it. Many are not--and that is the competitive edge these books offer the astute reader.

Published under the collective title of Foundations of Quantitative Finance, this set of ten books develops the advanced topics in mathematics that finance professionals need to advance their careers. These books expand the theory most do not learn in graduate finance programs, or in most financial mathematics undergraduate and graduate courses.

As an investment executive and authoritative instructor, Robert R. Reitano presents the mathematical theories he encountered and used in nearly three decades in the financial services industry and two decades in academia where he taught in highly respected graduate programs.

Readers should be quantitatively literate and familiar with the developments in the earlier books in the set. While the set offers a continuous progression through these topics, each title can be studied independently.

Features

• Extensively referenced to materials from earlier books
• Presents the theory needed to support advanced applications
• Supplements previous training in mathematics, with more detailed developments
• Built from the author's five decades of experience in industry, research, and teaching

Published and forthcoming titles in the Robert R. Reitano Quantitative Finance Series:

Book I: Measure Spaces and Measurable Functions

Book II: Probability Spaces and Random Variables

Book III: The Integrals of Lebesgue and (Riemann-)Stieltjes

Book IV: Distribution Functions and Expectations

Book V: General Measure and Integration Theory

Book VI: Densities, Transformed Distributions, and Limit Theorems

Book VII: Brownian Motion and Other Stochastic Processes

Book VIII: Itô Integration and Stochastic Calculus 1

Book IX: Stochastic Calculus 2 and Stochastic Differential Equations

Book X: Classical Models and Applications in Finance