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Advanced REIT portfolio optimizationinnovative tools for risk management /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advanced REIT portfolio optimizationby W. Brent Lindquist ... [et al.].
Reminder of title:	innovative tools for risk management /
other author:	Lindquist, W.
Published:	Cham :Springer International Publishing :2022.
Description:	xiv, 258 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Portfolio management.
Online resource:	https://doi.org/10.1007/978-3-031-15286-3
ISBN:	9783031152863$q(electronic bk.)

Advanced REIT portfolio optimizationinnovative tools for risk management /
Advanced REIT portfolio optimizationinnovative tools for risk management /[electronic resource] :by W. Brent Lindquist ... [et al.]. - Cham :Springer International Publishing :2022. - xiv, 258 p. :ill., digital ;24 cm. - Dynamic modeling and econometrics in economics and finance,v. 302363-8370 ;. - Dynamic modeling and econometrics in economics and finance ;v.17..

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.

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. "Markets for Real Estate Investment Trusts meet Modern Portfolio Theory and Quantitative Risk Management. This book takes you all the way from an in depth understanding of REIT markets to the application of up-to-date portfolio tools. A must-read for investors, practitioners, regulators and researchers alike." Paul Embrechts, Emeritus Professor of Mathematics, ETH Zurich "A modern and up to date analysis and tools for the post pandemic real estate investment market" Eduardo Schwartz, Ryan Beedie Chair in Finance, Beedie School of Business, Simon Fraser University "This book provides highly sophisticated tools for risk analysis and management for real estate investment trust investment. Institutional investors would be wise to employ the predictive methodologies set forth therein." Frank Fabozzi, Professor of Practice, Johns Hopkins University Carey Business School, Member EDHEC Risk Institute.

ISBN: 9783031152863$q(electronic bk.)

Standard No.: 10.1007/978-3-031-15286-3doiSubjects--Topical Terms:

186383
Portfolio management.

LC Class. No.: HG4529.5

Dewey Class. No.: 332.6

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505 0 $a 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.
520 $a 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. "Markets for Real Estate Investment Trusts meet Modern Portfolio Theory and Quantitative Risk Management. This book takes you all the way from an in depth understanding of REIT markets to the application of up-to-date portfolio tools. A must-read for investors, practitioners, regulators and researchers alike." Paul Embrechts, Emeritus Professor of Mathematics, ETH Zurich "A modern and up to date analysis and tools for the post pandemic real estate investment market" Eduardo Schwartz, Ryan Beedie Chair in Finance, Beedie School of Business, Simon Fraser University "This book provides highly sophisticated tools for risk analysis and management for real estate investment trust investment. Institutional investors would be wise to employ the predictive methodologies set forth therein." Frank Fabozzi, Professor of Practice, Johns Hopkins University Carey Business School, Member EDHEC Risk Institute.
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