2nd ed. — CRC Press, 2024. — 413 p. — (Advances in Applied Mathematics). — ISBN 1032262362.
The second edition of this successful and widely recognized textbook again focuses on
discrete topics. The author recognizes
two distinct paths of study and careers of actuarial science and financial engineering. This text can be very useful as a common core for
both. Therefore, there is substantial material in
Introduction to Financial Mathematics, Second Edition on the theory of
interest (the first half of the book), as well as the
probabilistic background necessary for the study of
portfolio optimization and derivative valuation (the second half). A course in multivariable calculus is
not required. The material
in the first two chapters should go a long way toward helping students prepare for the Financial Mathematics (FM) actuarial exam. Also, the discrete material will reveal how beneficial it is for the students to know more about
loans in their personal financial lives. The notable
changes and updates to this edition are itemized in the Preface, but overall, the presentation has been made more efficient. One example is the chapter on
discrete probability, which is rather unique in its emphasis on giving the deterministic problems studied earlier a probabilistic context. The section on
Markov chains, which is
not essential to the development, has been scaled down. Sample spaces and probability measures, random variables and distributions, expectation, conditional probability, independence, and estimation all follow.
Optimal portfolio selection coverage is
reorganized and the section on the practicalities of stock transactions has been
revised. Market portfolio and Capital Market Theory coverage is
expanded. New sections on Swaps and Value-at-Risk have been
added. This book, like the first edition, was written so that the print edition could stand alone. At times we simplify complicated algebraic expressions, or solve systems of linear equations, or numerically solve non-linear equations. Also, some attention is given to the use of
computer simulation to
approximate solutions to problems.
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