Introduction to Modern Portfolio Optimization with NuOPT, S-PLUS and S+Bayes

出版者:Springer

作者:Bernd Scherer

页数:428

出版时间:2007-09-21

价格:USD 79.95

装帧:Hardcover

isbn号码:9780387210162

具体描述

In 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&#8482; optimization module, the S-Plus Robust Library and the S+Bayes&#8482; 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.</P>

“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!”</P>

Steven P. Greiner, Ph.D., Chief Large Cap Quant & Fundamental Research Manager, Harris Investment Management</P>

“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.”</P>

Peter Knez, CIO, Global Head of Fixed Income, Barclays Global Investors</P>

“With regard to static portfolio optimization, the book gives a good survey on the development from the basic Markowitz approach to state of the art models and is in particular valuable for direct use in practice or for lectures combined with practical exercises.”</P>

Short Book Reviews of the International Statistical Institute, December 2005</P>

用户评价

这本书的封面设计给我留下了非常深刻的印象。它采用了一种非常简洁、现代的蓝白配色方案，给人一种专业、严谨的感觉。封面上最醒目的就是书名，字体选择得很有分寸，既不过于花哨，也不显得呆板。我尤其欣赏他们对排版的处理，整个布局非常平衡，信息层级清晰，即使是初次接触这个领域的读者也能一眼抓住重点。当然，书籍的装帧质量也很不错，纸张的手感扎实，印刷清晰，这对于一本需要频繁翻阅和做笔记的专业书籍来说至关重要。拿在手里，就能感受到它作为工具书的价值所在。我猜想，出版社在设计这个封面时，肯定也考虑到了目标读者的审美偏好——那些追求效率和清晰度的金融量化专业人士。这本书的“外表”成功地传达了一种专业性和可靠性，让人忍不住想翻开看看里面的内容是否同样出色。它不像某些教科书那样堆砌复杂的图表，而是通过克制的视觉语言，暗示了其内在的深度和实用性。

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这本书的语言风格散发出一种沉稳而又略带学者的幽默感。它不是那种高高在上、拒人于千里之外的学术论文腔调，读起来感觉作者像是一位经验丰富的同行在跟你娓娓道来。行文流畅自然，即使涉及到较为艰深的数学概念，作者也总能找到一种清晰易懂的类比或解释方式来辅助理解，避免了晦涩难懂的绕圈子。我特别欣赏它在解释复杂概念时所展现出的耐心。没有使用大量生僻的行话堆砌，而是注重用清晰的逻辑链条去构建知识体系。这种平易近人的叙事风格，极大地提高了阅读的舒适度和坚持度，让你愿意一口气读下去，而不是每读几页就需要停下来查阅其他资料来消化吸收。它成功地营造了一种“跟我一起探索这个领域”的氛围。

这本书的目录结构安排得极其巧妙，简直可以称得上是一份量化金融学习的路线图。它没有一开始就抛出最复杂的模型，而是循序渐进地引导读者进入主题。我注意到，前几章似乎花了不少篇幅在基础概念的梳理上，这对于我这种需要温习基础理论的人来说，简直是福音。然后，它自然而然地过渡到了核心的优化技术，这种逻辑上的连贯性极大地降低了学习的陡峭感。最让我眼前一亮的是，它似乎在理论讲解和实际操作之间找到了一个完美的平衡点。我感觉作者在组织章节时，非常注重知识的积累和串联，每一个新章节的引入，都像是为前一个章节的知识点做了一个有力的支撑，确保读者不会因为某个环节没跟上而感到掉队。这种精心设计的学习路径，远比那种简单罗列知识点的书籍要高明得多，它体现了作者对教学艺术的深刻理解。