Qin Uncertain Portfolio Optimization

1. Auflage 2016
ISBN: 978-981-10-1810-7
Verlag: Springer Singapore
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 192 Seiten, eBook

Reihe: Uncertainty and Operations Research

This book provides a new modeling approach for portfolio optimization problems involving a lack of sufficient historical data. The content mainly reflects the author’s extensive work on uncertainty portfolio optimization in recent years. Considering security returns as different variables, the book presents a series of portfolio optimization models in the framework of credibility theory, uncertainty theory and chance theory, respectively. As such, it offers readers a comprehensive and up-to-date guide to uncertain portfolio optimization models.

Zielgruppe

Research

Autoren/Hrsg.

Qin, Zhongfeng

Inhaltsverzeichnis

Preface1. Preliminaries1.1 Credibility Theory1.1.1 Credibility Measure1.1.2 Fuzzy Variables1.1.3 Independence1.1.4 Expected Value1.1.5 Variance1.2 Uncertainty Theory1.2.1 Uncertain Measure1.2.2 Uncertain Variable1.2.3 Uncertainty Distribution1.2.4 Operational Law1.2.5 Expected Value1.3 Genetic Algorithm2. Credibilistic Mean-Variance-Skewness2.1 Introduction2.2 Skewness of Fuzzy Variable2.3 Mean-Variance-Skewness Model2.4 Fuzzy Simulation2.4.1 Fuzzy Simulation for Credibility2.4.2 Fuzzy Simulation for Expected Value2.4.3 Fuzzy Simulation for Skewness2.5 Numerical Examples3. Credibilistic Mean-Absolute Deviation Model3.1 Introduction3.2 Absolute Deviation of Fuzzy Variable3.3 Mean-Absolute Deviation Model3.4 Fuzzy Simulation for Absolute Deviation3.5 Numerical Examples4. Minimization Model4.1 Introduction4.2 Cross-Entropy of Fuzzy Variable4.3 Credibilistic Cross-Entropy Minimization Model4.4 Fuzzy Simulation for Cross-Entropy4.5 Numerical Examples5. Uncertain Mean-Semiabsolude Deviation Model5.1 Introduction5.2 Semiabsolute Deviation of Uncertain Variable5.3 Mean Semiabsolute Deviation Model5.4 Mean-Semiabsolute Deviation Adjusting Model5.5 Numerical Examples6. Uncertain Mean-LPMs Model6.1 Introduction6.2 Lower Partial Moments of Uncertain Variable6.3 Uncertain Mean-LPMs Model6.4 Numerical Examples7. Interval Mean-Semiabsolute Deviation Model7.1 Introduction7.2 Uncertainty Generation Theorem7.3 Mean-Semiabsolute Deviation Model7.4 Numerical Examples8. Uncertain Random Mean-Variance Model8.1 Introduction8.2 Uncertain Random Variable8.3 Problem Statement8.4 Model Formulation8.5 Numerical Examples9. Fuzzy Random Mean-Variance Adjusting Model9.1 Introduction9.2 Fuzzy Random Variable9.3 Mean-Variance Adjusting Model9.4 Equivalent Crisp Models9.5 Numerical Examples10. Random Fuzzy Mean-Risk Model10.1 Introduction10.2 Random Fuzzy Variable10.3 Absolute Deviation of Random Fuzzy Variable10.4 Semivariance of Random Fuzzy Variable10.5 Random Fuzzy Mean-Risk Models10.5.1 Random Fuzzy Mean-Absolute Deviation Model<10.5.2 Random Fuzzy Mean-Semivariance Model10.6 Random Fuzzy Simulation10.7 Numerical ExamplesBibliographyList of Frequently Used Symbols

Über Autor(innen)

Zhongfeng Qin received his BS degree from Nankai University, Tianjin, China and his PhD degree in Operations Research and Cybernetics from Tsinghua University, Beijing, China. He is currently an associate professor at the School of Economics and Management at Beihang University, Beijing, China. His current research interests include uncertain modeling and optimization, portfolio optimization and risk modeling. He was awarded “New Century Excellent Talents in University of the Ministry of Education” in 2012. Also, he was honored with the “7th Jiaqing Zhong Prize on Operations Research” and the “9th Outstanding New Scholar on Operations Research” award.