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Full Description
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
Contents
1. Distribution and Density Functions. 2. Transformed Random Variables.3. Order Statistics. 4. EXpectations of Random Variables 1. 5. Simulating Samples of RVs - Examples. 6. Limit Theorems. 7. Estimating Tail Events 2
