Author Peter Jackel doesn't seem to have much to say about Monte Carlo methods in finance. Excluding the index, the book is just 210 pages long, and he spends most of those pages seeming to "run out the clock."

Monte Carlo methods are mentioned briefly in two opening chapters, but the author spends most of those chapters on topics unrelated to the title of the book. He offers brief descriptions of numerous probability distributions—Bernoulli, Poisson, gamma, exponential, Cauchy, Student-t, Pareto ... One gets the impression he will be using them all in later chapters, but he doesn't. He is simply listing probability distributions. The highly technical Feynman-Kac theorem is introduced, but it too isn't used for anything later in the book.

Chapters 3 through 6 discuss topics such as standard stochastic processes, correlation and related metrics of co-dependence, and techniques to "recover" estimated covariance matrices that are not positive semi-definite. Discussions tend to be cryptic, needlessly technical and non rigorous. Also, Jackel doesn't tie them in well to finance and the Monte Carlo method.

Chapters 8, 9 and 10 cover pseudorandom numbers, low-discrepancy numbers, and non-uniform variates. Coverage of the topics is spotty and at times quite frustrating. For example, Jackel proposes the use of the logistic map to generate pseudorandom numbers. He then delves into an elaborate discussion of this, mentioning chaos theory and presenting graphs that I don't understand. Finally, he concludes that the resulting generator has severe shortcomings that argue against its use—something researchers concluded decades ago. Why did he even raise the subject? He briefly recommends several pseudorandom number generators. I happen to be familiar with these generators. They might have been state-of-the-art 25 years ago, but they are not today. Having spent pages describing the useless logistic generator, the author declines to describe recommended generators, instructing us to read the research papers ourselves.

Chapter 10 covers variance reduction and related techniques. Some discussions are better than others. A few are just hand waving. Brownian bridges and related techniques for generating paths are covered, and their treatment is quite nice.