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The new edition of this influential textbook, geared towards
graduate or advanced undergraduate students, teaches the statistics
necessary for financial engineering. In doing so, it illustrates
concepts using financial markets and economic data, R Labs with
real-data exercises, and graphical and analytic methods for
modeling and diagnosing modeling errors. These methods are critical
because financial engineers now have access to enormous quantities
of data. To make use of this data, the powerful methods in this
book for working with quantitative information, particularly about
volatility and risks, are essential. Strengths of this
fully-revised edition include major additions to the R code and the
advanced topics covered. Individual chapters cover, among other
topics, multivariate distributions, copulas, Bayesian computations,
risk management, and cointegration. Suggested prerequisites are
basic knowledge of statistics and probability, matrices and linear
algebra, and calculus. There is an appendix on probability,
statistics and linear algebra. Practicing financial engineers will
also find this book of interest.
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