Modern Portfolio Optimization with NuOPT™, S-PLUS®, and S+Bayes™ (eBook)

Bernd Scherer, R. Douglas Martin (Autoren)

eBook Download: PDF

2007 | 2005
XXII, 406 Seiten
Springer New York (Verlag)
978-0-387-27586-4 (ISBN)

Infos

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. This trend will only accelerate in the coming years. This practical handbook fills the gap between current university instruction and current industry practice. It provides a comprehensive computationally-oriented treatment of modern portfolio optimization and construction methods using the powerful NUOPT for S-PLUS optimizer.

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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(R), the S+NuOPT(TM) optimization module, the S-Plus Robust Library and the S+Bayes(TM) Library, along with about 100 S-Plus scripts and some CRSP(R) 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 InvestmentManagement 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 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. Short Book Reviews of the International Statistical Institute,  December 2005

Inhalt

Preface 6
Purpose of Book 6
Intended Audience 7
Organization of the Book 7
Downloading the Software and Data 10
Using the Scripts and Data 11
Acknowledgments 12
Contents 14
List of Code Examples 17
Linear and Quadratic Programming 20
1.1 Linear Programming: Testing for Arbitrage 20
1.2 Quadratic Programming: Balancing Risk and Return 25
1.3 Dual Variables and the Impact of Constraints 36
1.4 Analysis of the Efficient Frontier 43
Exercises 49
Endnotes 51
General Optimization with SIMPLE 53
2.1 Indexing Parameters and Variables 53
2.2 Function Optimization 63
2.3 Maximum Likelihood Optimization 68
2.4 Utility Optimization 72
2.5 Multistage Stochastic Programming 79
2.6 Optimization within S-PLUS 87
Exercises 97
Endnotes 98
Advanced Issues in Mean- Variance Optimization 99
3.1 Nonstandard Implementations 99
3.2 Portfolio Construction and Mixed-Integer Programming 108
3.3 Transaction Costs 116
Exercises 124
Endnotes 126
Resampling and Portfolio Choice 127
4.1 Portfolio Resampling 127
4.2 Resampling Long-Only Portfolios 132
4.3 Introduction of a Special Lottery Ticket 133
4.4 Distribution of Portfolio Weights 138
4.5 Theoretical Deficiencies of Portfolio Construction via Resampling 144
4.6 Bootstrap Estimation of Error in Risk- Return Ratios 147
Exercises 154
Endnotes 157
Scenario Optimization: Addressing Non- normality 159
5.1 Scenario Optimization 159
5.2 Mean Absolute Deviation 171
5.3 Semi-variance and Generalized Semi- variance Optimization 176
5.4 Probability-Based Risk/Return Measures 182
5.5 Minimum Regret 188
5.6 Conditional Value-at-Risk 192
5.7 CDO Valuation using Scenario Optimization 207
Exercises 211
Endnotes 212
Robust Statistical Methods for Portfolio Construction 213
6.1 Outliers and Non-normal Returns 213
6.2 Robust Statistics versus Classical Statistics 218
6.3 Robust Estimates of Mean Returns 220
6.4 Robust Estimates of Volatility 227
6.5 Robust Betas 236
6.6 Robust Correlations and Covariances 239
6.7 Robust Distances for Determining Normal Times versus Hectic Times 244
6.8 Robust Covariances and Distances with Different Return Histories 251
6.9 Robust Portfolio Optimization 256
6.10 Conditional Value-at-Risk Frontiers: Classical and Robust 279
6.11 Influence Functions for Portfolios 294
Exercises 312
Endnotes 315
Bayes Methods 317
7.1 The Bayesian Modeling Paradigm 317
7.2 Bayes Models for the Mean and Volatility of Returns 321
7.3 Bayes Linear Regression Models 364
7.4 Black-Litterman Models 377
7.5 Bayes-Stein Estimators of Mean Returns 393
7.6 Appendix 7A: Inverse Chi-Squared Distributions 398
7.7 Appendix 7B: Posterior Distributions for Normal Likelihood Conjugate Priors 402
7.8 Appendix 7C: Derivation of the Posterior for Jorion’s Empirical Bayes Estimate 402
Exercises 405
Endnotes 407
Bibliography 410
Index 417

Produkt-Details

• Erscheint lt. Verlag: 5.9.2007
• Zusatzinfo: XXII, 406 p.
• Verlagsort: New York
• Sprache: englisch
• Themenwelt: Mathematik / Informatik ► Informatik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Angewandte Mathematik
• Themenwelt: Mathematik / Informatik ► Mathematik ► Wahrscheinlichkeit / Kombinatorik
• Themenwelt: Recht / Steuern ► Wirtschaftsrecht
• Themenwelt: Technik
• Themenwelt: Wirtschaft ► Allgemeines / Lexika
• Themenwelt: Wirtschaft ► Betriebswirtschaft / Management ► Finanzierung
• Schlagworte: Investment • Investmentmanagement • linear optimization • Optimization • Portfolio • portfolio optimization • Portfolio Theory • Quantitative Finance • resampling • Sets • Statistica • statistical method • Variance
• ISBN-10: 0-387-27586-X / 038727586X
• ISBN-13: 978-0-387-27586-4 / 9780387275864