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E-grāmata: Portfolio Optimization

4.25/5 (7 ratings by Goodreads)
(University of Waterloo, Ontario, Canada)
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Portfolio OptimizationPalielināt
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Eschewing a more theoretical approach, Portfolio Optimization shows how the mathematical tools of linear algebra and optimization can quickly and clearly formulate important ideas on the subject. This practical book extends the concepts of the Markowitz "budget constraint only" model to a linearly constrained model.

Only requiring elementary linear algebra, the text begins with the necessary and sufficient conditions for optimal quadratic minimization that is subject to linear equality constraints. It then develops the key properties of the efficient frontier, extends the results to problems with a risk-free asset, and presents Sharpe ratios and implied risk-free rates. After focusing on quadratic programming, the author discusses a constrained portfolio optimization problem and uses an algorithm to determine the entire (constrained) efficient frontier, its corner portfolios, the piecewise linear expected returns, and the piecewise quadratic variances. The final chapter illustrates infinitely many implied risk returns for certain market portfolios.

Drawing on the authors experiences in the academic world and as a consultant to many financial institutions, this text provides a hands-on foundation in portfolio optimization. Although the author clearly describes how to implement each technique by hand, he includes several MATLAB® programs designed to implement the methods and offers these programs on the accompanying downloadable resources.

Recenzijas

Michael Bests book is the ideal combination of optimization and portfolio theory. Mike has provided a wealth of practical examples in MATLAB to give students hands-on portfolio optimization experience. The included stand-alone MATLAB code even provides its own quadratic solver, so that students do not need to rely on any external packages. David Starer, Stevens Institute of Technology

Overall, this is a nice book that would be ideal as a textbook for one-semester portfolio optimization courses. It can also be good as a supplementary text for courses in operations research and/or financial engineering. The book is self-contained enough to be used as study material for those who want to teach themselves portfolio optimization and related computer programming, be they advanced undergraduate students, graduate students, or financial practitioners. Youngna Choi, Mathematical Reviews, Issue 2012a

an excellent companion text for the course Discrete-Time Models in Finance that I have been teaching in the past years. I think adding your text can make the course more lively. This is what I plan to do in the coming (fall) semester. Edward P. Kao, University of Houston, Texas, USA

Preface ix
Acknowledgments xii
About the Author xii
Optimization
1(20)
Quadratic Minimization
1(7)
Nonlinear Optimization
8(4)
Extreme Points
12(3)
Computer Results
15(3)
Exercises
18(3)
The Efficient Frontier
21(20)
The Efficient Frontier
21(12)
Computer Programs
33(3)
Exercises
36(5)
The Capital Asset Pricing Model
41(18)
The Capital Market Line
41(10)
The Security Market Line
51(3)
Computer Programs
54(4)
Exercises
58(1)
Sharpe Ratios and Implied Risk Free Returns
59(22)
Direct Derivation
60(6)
Optimization Derivation
66(7)
Free Problem Solutions
73(2)
Computer Programs
75(3)
Exercises
78(3)
Quadratic Programming Geometry
81(26)
The Geometry of QPs
81(5)
Geometry of QP Optimality Conditions
86(6)
The Geometry of Quadratic Functions
92(4)
Optimality Conditions for QPs
96(7)
Exercises
103(4)
A QP Solution Algorithm
107(32)
QPSolver: A QP Solution Algorithm
108(19)
Computer Programs
127(9)
Exercises
136(3)
Portfolio Optimization with Constraints
139(26)
Linear Inequality Constraints: An Example
140(11)
The General Case
151(8)
Computer Results
159(4)
Exercises
163(2)
Determination of the Entire Efficient Frontier
165(26)
The Entire Efficient Frontier
165(18)
Computer Results
183(6)
Exercises
189(2)
Sharpe Ratios under Constraints, and Kinks
191(24)
Sharpe Ratios under Constraints
191(8)
Kinks and Sharpe Ratios
199(12)
Computer Results
211(2)
Exercises
213(2)
Appendix 215(2)
References 217(4)
Index 221
Michael J. Best is a professor in the Department of Combinatorics and Optimization at the University of Waterloo in Ontario, Canada. He received his Ph.D. from the Department of Industrial Engineering and Operations Research at the University of California, Berkeley. Dr. Best has authored over 37 papers on finance and nonlinear programming and co-authored a textbook on linear programming. He also has been a consultant to Bank of America, Ibbotson Associates, Montgomery Assets Management, Deutsche Bank, Toronto Dominion Bank, and Black Rock-Merrill Lynch.
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