Jan 17, 2011 | Comments 31
David Ruppert, “Statistics and Facts Analysis for Financial Engineering”
S.r | 2010 | ISBN: 1441977864 | 638 pages | File kind: PDF | 11,4 mb
Financial engineers have access to enormous quantities of facts but will need powerful methods for extracting quantitative facts, especially about volatility and risks. Key features of this textbook are: illustration of concepts with financial markets and economic facts, R Labs with real-data exercises, and integration of graphical and analytic methods for modeling and diagnosing modeling errors. Despite some overlap with the author’s undergraduate textbook Statistics and Finance: An Introduction, this book differs from that earlier volume in quite a few critical aspects: it’s graduate-level; computations and graphics are performed in R; and numerous advanced topics are covered, for example, multivariate distributions, copulas, Bayesian computations, VaR and expected shortfall, and cointegration. The prerequisites are fundamental statistics and probability, matrices and linear algebra, and calculus. Some exposure to finance is useful.
David Ruppert is Andrew Schultz, Jr., Professor of Engineering and Professor of Statistical Science, School of Operations Study and Facts Engineering, Cornell University, where he teaches statistics and financial engineering and is a member of the Program in Financial Engineering. His study areas consist of asymptotic theory, semiparametric regression, functional facts analysis, biostatistics, model calibration, measurement error, and astrostatistics. Professor Ruppert received his PhD in Statistics at Michigan State University. He is a Fellow of the American Statistical Association plus the Institute of Mathematical Statistics and won the Wilcoxon prize. He is Editor of the Electronic Journal of Statistics, former Editor of the Institute of Mathematical Statistics’s Lecture NotesMonographs Series, and former Associate Editor of quite a few main statistics journals. Professor Ruppert has published over 100 scientific papers and four books: Transformation and Weighting in Regression, Measurement Error in Nonlinear Models, Semiparametric Regression, and Statistics and Finance: An Introduction.
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