000			01774nam a22002417a 4500
999			_c3538 _d3538
005			20221114133253.0
008			221114b ||||| |||| 00| 0 eng d
020			_a9781493951734
082			_a658.15 _bRUP
100			_aRuppert, David _94822
245			_aStatistics and data analysis for financial engineering : _bwith R examples
250			_a2nd
260			_bSpringer _aNew York _c2015
300			_axxvi, 719 p.
365			_aEURO _b64.99
520			_aAbout this book 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. Financial engineers now have access to enormous quantities of data. To make use of these data, the powerful methods in this book, 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, multivariate volatility 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.
650			_aFinance--Statistical methods _98437
650			_aStatistics _9951
650			_aEconomics _9722
650			_aMathematical statistics _9837
700			_aMatteson, David S. _99998
942			_2ddc _cBK