As we all know by now, the field of quantitative finance is large and immensely popular among mathematicians, physicists, computer scientists, and of course finance people. It is safe to say that, by now, there are numerous books available explaining the theory, the models, and computational aspects of quantitative finance. It is, on the other hand, harder to find a reference that will guide the reader through the theory and methods in an applied way, with some computational code to learn from. Implementing Models in Quantitative Finance fills this gap in an efficient way.
As the title suggests the book is divided into two parts.
Part I — Methods
Part one is concentrated on presenting various numerical methods used in problems encountered in quantitative finance. These include Monte Carlo methods, dynamic programming, finite difference methods, numerical solutions of linear systems, quadrature methods, the Laplace transform and copula functions. There is not much formality in the discussion. The authors conecntrate on providing narrative introductions, detailed algorithms, appropriate illustrations and mathematical presentation (formulas) when needed. They strike the right balance on mathematical exposition so as not to crowd the text or dismay the reader, who can instead concentrate on figuring out how to apply each method and how to work it out computationally. Usually, at least in my case, this was an easier way of learning, i.e. by doing it and hence understanding it. Along with the algorithms, the authors present the code, which is always a plus when reading an applied book.
Part II — Problems
Second part of the book deals with problems, i.e. specific cases. There are really a handful of problems ranging from portfolio management and trading, vanilla options, exotic derivatives, interest rate and credit derivatives to financial econometrics. All the problems are structured in the following way:
- problem statement
- solution methodology
- implementation and algorithm
- results and comments
In the problem statement, authors introduce and present the problem in detail, discussing its significance, history and some research results (of course with appropriate references). Mathematical description and presentation is provided in solution methodology and in the implementation and algorithm sections. The presentation is very detailed and easy to follow — and, most importantly, it is easy to replicate the steps and produce the code. The results are commented in detail with full illustrations and tables.
The style of the book is very inviting and it should be on the shelf of every serious researcher and practitioner in quantitative finance, including graduate students. Teachers could easily use the book in their applied courses. Overall, I think the book is a clear self-contained guide to implementing models in quantitative finance and as such it is going to be very popular in quant and academic circles.
Ita Cirovic Donev holds a Masters degree in statistics from Rice University. Her main research areas are in mathematical finance; more precisely, statistical methods for credit and market risk. Apart from the academic work she does statistical consulting work for financial institutions in the area of risk management.