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020 _a9780387275864
_9978-0-387-27586-4
024 7 _a10.1007/9780387275864
_2doi
035 _avtls000330419
039 9 _a201509030439
_bVLOAD
_c201404121712
_dVLOAD
_c201404091450
_dVLOAD
_c201401311340
_dstaff
_y201401291454
_zstaff
_wmsplit0.mrc
_x839
050 4 _aQA276-280
100 1 _aScherer, Bernd.
_eautor
_9302714
245 1 0 _aIntroduction to Modern Portfolio optimization with NUOPT and S-PLUS /
_cby Bernd Scherer, R. Douglas Martin.
264 1 _aNew York, NY :
_bSpringer New York,
_c2005.
300 _aXXII, 410 páginas, 161 illus.
_brecurso en línea.
336 _atexto
_btxt
_2rdacontent
337 _acomputadora
_bc
_2rdamedia
338 _arecurso en línea
_bcr
_2rdacarrier
347 _aarchivo de texto
_bPDF
_2rda
500 _aSpringer eBooks
505 0 _aLinear and Quadratic Programming -- General Optimization With Simple -- Advanced Issues in Mean-Variance Optimization -- Resampling and Portfolio Choice -- Scenario Optimization: Addressing Non-normality -- Robust Statistical Methods for Portfolio Construction -- Bayes Methods.
520 _aIn recent years portfolio optimization and construction methodologies have become an increasingly critical ingredient of asset and fund management, while at the same time portfolio risk assessment has become an essential ingredient in risk management, and this trend will only accelerate in the coming years. Unfortunately there is a large gap between the limited treatment of portfolio construction methods that are presented in most university courses with relatively little hands-on experience and limited computing tools, and the rich and varied aspects of portfolio construction that are used in practice in the finance industry. Current practice demands the use of modern methods of portfolio construction that go well beyond the classical Markowitz mean-variance optimality theory and require the use of powerful scalable numerical optimization methods. This book fills the gap between current university instruction and current industry practice by providing a comprehensive computationally-oriented treatment of modern portfolio optimization and construction methods. The computational aspect of the book is based on extensive use of S-Plus®, the S+NuOPT™ optimization module, the S-Plus Robust Library and the S+Bayes™ Library, along with about 100 S-Plus scripts and some CRSP® sample data sets of stock returns. A special time-limited version of the S-Plus software is available to purchasers of this book. "For money managers and investment professionals in the field, optimization is truly a can of worms rather left un-opened, until now! Here lies a thorough explanation of almost all possibilities one can think of for portfolio optimization, complete with error estimation techniques and explanation of when non-normality plays a part. A highly recommended and practical handbook for the consummate professional and student alike!" Steven P. Greiner, Ph.D. Chief Large Cap Quant & Fundamental Research Manager Harris Investment Management "The authors take a huge step in the long struggle to establish applied post-modern portfolio theory. The optimization and statistical techniques generalize the normal linear model to include robustness, non-normality, and semi-conjugate Bayesian analysis via MCMC. The techniques are very clearly demonstrated by the extensive use and tight integration of S-Plus software. Their book should be an enormous help to students and practitioners trying to move beyond traditional modern portfolio theory." Peter Knez CIO, Global Head of Fixed Income Barclays Global Investors
590 _aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700 1 _aMartin, R. Douglas.
_eautor
_9302715
710 2 _aSpringerLink (Servicio en línea)
_9299170
776 0 8 _iEdición impresa:
_z9780387210162
856 4 0 _uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-0-387-27586-4
_zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
942 _c14
999 _c278816
_d278816