Heavy-Tail Phenomena: Probabilistic and Statistical Modeling (Springer Series in Operations Research and Financial Engineering) Review

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(More customer reviews)Sid Resnick taught me stochastic processes when I was a graduate student at Stanford. He is an exceptional lecturer who really stimulates the students. I don't think I ever had a boring lecture from him even though probability theory and stochastic processes can at times be very dry subjects.
Over the many years since then Sid has moved on and spent many fruitful years at Colorado State and now Cornell. In addition to his many excellent papers and his fine collaborative work with Richard Davis, he has written a large number of very interesting and thought provoking texts on extreme value theory, stochastic processes and probability theory. I have a deep appreciation for his contributions to the theory of extremes as that has also been one of my research areas and was the topic of my Ph.D. dissertation. This is the second outstanding book Resnick has written on extremes. This one has more of a modelling flavor to it with an eye toward financial applications. It seems these days that much of the research in time series modelling and stochastic processes is motivated by applications in finance. This is certainly also the case with extreme value models as can be seen by the many fine books on extremes that have appeared recently.
This book shows the theory and applications of models for heavy-tailed distributions. Resnick makes a very good point about insurance claim cost. This was certainly a phenomena I had to deal with when modeling workers compensation insurance claims at Risk Data Corporation. It is also interesting to see coverage about what is needed to consistently estimate extreme values by bootstrapping.
Time series models that deal with heavy-tailed distributions are also mentioned.

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This comprehensive text gives an interesting and useful blend of the mathematical, probabilistic and statistical tools used in heavy-tail analysis. It is uniquely devoted to heavy-tails and emphasizes both probability modeling and statistical methods for fitting models.Prerequisites for the reader include a prior course in stochastic processes and probability, some statistical background, some familiarity with time series analysis, and ability to use a statistics package. This work will serve second-year graduate students and researchers in the areas of applied mathematics, statistics, operations research, electrical engineering, and economics.

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