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Titulos:
Statistics and data analysis for financial engineering : with R examples / David Ruppert, David S. Matteson.
ISBN:
9781493926138; 1493926136; 1441977864 (cloth); 9781441977861 (cloth); 1441977880 (paper); 9781441977885 (paper); 1461427495 (paper); 9781461427490 (paper); 9781493926145 (ebook); 1493926144 (ebook)
Lugar de Edición:
New York :
Editor:
Springer-Verlag,
Fecha de Edición:
c2015.
Edición #:
2nd ed.
Notas Formateada:
Introduction -- Returns -- Fixed income securities -- Exploratory data analysis -- Modeling univariate distributions -- Resampling -- Multivariate statistical models -- Copulas -- Time series models: basics -- Time series models: further topics -- Portfolio theory -- Regression: basics -- Regression: troubleshooting -- Regression: advanced topics -- Cointegration -- The capital asset pricing model -- Factor models and principal components -- GARCH models -- Risk management -- Bayesian data analysis and MCMC -- Nonparametric regression and splines.
Nota de contenido:
"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." --Contratapa.
Palabras clave:
Financial engineering; Finance; Ingeniería financiera; Finanzas

Leader:
nam
Campo 008:
150828s2015 nyua b 001 0 eng d
Campo 020:
^a9781493926138
Campo 020:
^a1493926136
Campo 020:
^a1441977864 (cloth)
Campo 020:
^a9781441977861 (cloth)
Campo 020:
^a1441977880 (paper)
Campo 020:
^a9781441977885 (paper)
Campo 020:
^a1461427495 (paper)
Campo 020:
^a9781461427490 (paper)
Campo 020:
^a9781493926145 (ebook)
Campo 020:
^a1493926144 (ebook)
Campo 035:
^a(OCoLC)000064550
Campo 035:
^a(udesa)000064550USA01
Campo 035:
^a(OCoLC)919315197
Campo 035:
^a(OCoLC)990000645500204151
Campo 040:
^aU@S^bspa^cU@S
Campo 100:
1 ^aRuppert, David,^d1948-
Campo 245:
10^aStatistics and data analysis for financial engineering :^bwith R examples /^cDavid Ruppert, David S. Matteson.
Campo 246:
Campo 250:
^a2nd ed.
Campo 260:
^aNew York :^bSpringer-Verlag,^cc2015.
Campo 300:
^axxvi, 719 p. :^bil. ;^c25 cm.
Campo 490:
1 ^aSpringer texts in statistics
Campo 505:
0 ^aIntroduction -- Returns -- Fixed income securities -- Exploratory data analysis -- Modeling univariate distributions -- Resampling -- Multivariate statistical models -- Copulas -- Time series models: basics -- Time series models: further topics -- Portfolio theory -- Regression: basics -- Regression: troubleshooting -- Regression: advanced topics -- Cointegration -- The capital asset pricing model -- Factor models and principal components -- GARCH models -- Risk management -- Bayesian data analysis and MCMC -- Nonparametric regression and splines.
Campo 520:
^a"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." --Contratapa.
Campo 650:
0^aFinancial engineering^xStatistical methods.
Campo 650:
0^aFinance^xStatistical methods.
Campo 650:
7^aIngeniería financiera^xMétodos estadísticos.^2UDESA
Campo 650:
7^aFinanzas^xMétodos estadísticos.^2UDESA
Campo 700:
1 ^aMatteson, David S.
Proveniencia:
^aUniversidad de San Andrés - Biblioteca Max Von Buch
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Institucion:
Universidad de San Andrés
Dependencia:
Biblioteca Max Von Buch

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