Foundations of quantitative finance, book II: probability spaces and random variables

Table of content:
Preface

Introduction

1. Probability Spaces

2. Limit Theorems on Measurable Sets

3. Random Variables and Distribution Functions

4. Samples of Random Variables

5. Limit Theorems for Random Variable Sequences

6. Distribution Functions and Borel Measures

7. Copulas and Sklar's Theorem

8. Weak Convergence of Distribution Functions

9. Estimating Tail Events

References

Index

[https://www.routledge.com/Foundations-of-Quantitative-Finance-Book-II--Probability-Spaces-and-Random-Variables/Reitano/p/book/9781032197173]

Every financial professional wants and needs an advantage. A firm foundation in advanced mathematics can translate into dramatic advantages to professionals willing to obtain it. Many are not—and that is the advantage these books offer the astute reader.

Published under the collective title of Foundations of Quantitative Finance, this set of ten books presents the advanced mathematics finance professionals need to advantage their careers, these books present the theory most do not learn in graduate finance programs, or in most financial mathematics undergraduate and graduate courses.

As a high-level industry executive and authoritative instructor, Robert R. Reitano presents the mathematical theories he encountered in nearly three decades working in the financial industry and two decades teaching in highly respected graduate programs.

Readers should be quantitatively literate and familiar with the developments in the first book in the set, Foundations of Quantitative Finance Book I: Measure Spaces and Measurable Functions.

(https://www.routledge.com/Foundations-of-Quantitative-Finance-Book-II--Probability-Spaces-and-Random-Variables/Reitano/p/book/9781032197173)