Advanced REIT Portfolio Optimization
Innovative Tools for Risk Management
(Sprache: Englisch)
This book provides an investor-friendly presentation of the premises and applications of the quantitative finance models governing investment in one asset class of publicly traded stocks, specifically real estate investment trusts (REITs). The models...
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Klappentext zu „Advanced REIT Portfolio Optimization “
This book provides an investor-friendly presentation of the premises and applications of the quantitative finance models governing investment in one asset class of publicly traded stocks, specifically real estate investment trusts (REITs). The models provide highly advanced analytics for REIT investment, including:- portfolio optimization using both historic and predictive return estimation;
- model backtesting; a complete spectrum of risk assessment and management tools with an emphasis on early warning systems, risk budgeting, estimating tail risk, and factor analysis;
- derivative valuation;
- and incorporating ESG ratings into REIT investment.
These quantitative finance models are presented in a unified framework consistent with dynamic asset pricing (rational finance). Given its scope and practical orientation, this book will appeal to investors interested in portfolio optimization and innovative tools for investment risk assessment.
Inhaltsverzeichnis zu „Advanced REIT Portfolio Optimization “
Chapter 1. The Real Estate Investment Market: The Current State and Why Advances Are Needed.- Chapter 2. The Data.- Chapter 3. Modern Portfolio Theory.- Chapter 4. Historical Portfolio Optimization - Domestic REITs.- Chapter 5. Diversification with International REITs.- Chapter 6. Black-Litterman Optimization Results.- Chapter 7. Dynamic Portfolio Optimization: Beyond MPT.- Chapter 8. Backtesting.- Chapter 9. Diversification with Real Estate Stocks.- Chapter 10. Risk Information and Management.- Chapter 11. Optimization with Performance-Attribution Constraints.- Chapter 12. Option Pricing.- Chapter 13. Inclusion of ESG Ratings in Optimization.- Chapter 14. Inclusion of ESG Ratings in Option Pricing.
Autoren-Porträt von W. Brent Lindquist, Svetlozar T. Rachev, Yuan Hu, Abootaleb Shirvani
Prof. W. Brent Lindquist is a computational mathematician at Texas Tech University (USA). He has developed numerical methods for portfolio optimization, flow in porous media, 3D image analysis, Riemann problems, hierarchy formation in social groups, and quantum electrodynamics. He was a co-founder of a petroleum software company and has commercially licensed his image analysis code.Yuan Hu received her Ph.D. from Texas Tech University (USA) in 2022. Her current research considers approaches to discrete option pricing; risk management and option valuation of crypto assets; and portfolio optimization constrained by performance attribution. She is currently the Stefan E. Warschawski Visiting Assistant Professor in the Department of Mathematics at the University of California San Diego (USA).
Dr. Abootaleb Shirvani received his Ph.D. from Texas Tech University (USA) in 2021. His general research interests include financial mathematics, statistics, and actuarial mathematics. He is currently an assistant professor in Statistics and Actuarial Science in the Department of Mathematics at Kean University (USA).
Prof. Svetlozar (Zari) Rachev is a Professor at the Department of Mathematics and Statistics at Texas Tech University (USA) and one of the world's foremost authorities in the application of heavy-tailed distributions in finance. He was a co-founder and President of Bravo Risk Management Group, originator of the Cognity methodology. Bravo was acquired by FinAnalytica, where Zari served as Chief Scientist.
Bibliographische Angaben
- Autoren: W. Brent Lindquist , Svetlozar T. Rachev , Yuan Hu , Abootaleb Shirvani
- 2022, 1st ed. 2022, XIV, 258 Seiten, Masse: 15,5 x 23,5 cm, Gebunden, Englisch
- Verlag: Springer, Berlin
- ISBN-10: 3031152859
- ISBN-13: 9783031152856
Sprache:
Englisch
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