Advanced REIT Portfolio Optimization: Innovative Tools for Risk Management

✍ Scribed by W. Brent Lindquist, Svetlozar T. Rachev, Yuan Hu, Abootaleb Shirvani

• Publisher: Springer
• Year: 2022
• Tongue: English
• Leaves: 268
• Series: Dynamic Modeling and Econometrics in Economics and Finance, 30
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

Foreword
About This Book
Contents
Abbreviations
Chapter 1: The Real Estate Investment Market: The Current State and Why Advances Are Needed
References
Chapter 2: The Data
2.1 REIT Asset Descriptions
2.1.1 Domestic REITs
2.1.2 International REITs
2.2 Real Estate Stock Descriptions
2.3 Benchmarks
2.3.1 Indices
2.3.2 Exchange Traded Funds
2.3.3 Mutual Funds
2.4 Additional Assets and Indices
2.5 Data Observations
References
Chapter 3: Modern Portfolio Theory
3.1 Return Time Series
3.2 MPT-Based Portfolios
3.2.1 Markowitz Mean-Variance Portfolio
3.2.2 Capital Market Line and the Markowitz Mean-Variance Tangent Portfolio
3.2.3 CVaR-Minimizing Portfolios
3.2.4 Capital Market Line and the CVaRα Tangent Portfolio
3.2.5 Criticisms of Mean-Variance Optimization
3.3 Black-Litterman Model
3.4 Historical Optimization
References
Chapter 4: Historical Portfolio Optimization: Domestic REITs
4.1 Basic Strategies, Price, and Return Performance
4.1.1 Long-Only Strategy
4.1.2 Jacobs et al. Long-Short Strategy
4.1.3 Lo-Patel Long-Short Strategy
4.1.4 Long-Short Momentum Strategy
4.2 Performance Under Turnover Constraints
4.3 Performance-Risk Measures
4.4 Observations
References
Chapter 5: Diversification with International REITs
5.1 International Portfolio Performance
5.1.1 Long-Only International Portfolios
5.1.2 Jacobs et al. Long-Short International Portfolios
5.1.3 Lo-Patel Long-Short International Portfolios
5.2 Global Portfolio Performance
5.2.1 Long-Only Global Portfolios
5.2.2 Jacobs et al. Long-Short Global Portfolios
References
Chapter 6: Black-Litterman Optimization Results
6.1 Domestic Portfolios
6.2 Global Portfolios
Chapter 7: Dynamic Portfolio Optimization: Beyond MPT
7.1 Dynamic Optimization
7.1.1 ARMA(1,1)-GARCH(1,1) with Student´s t-Distribution
7.1.2 Multivariate t-Distribution and t-Copulas
7.1.3 Generation of Dynamic Returns
7.1.4 Combining the Dynamic Approach with Black-Litterman Optimization
7.2 Portfolio Optimization Using Dynamic Returns
7.2.1 Dynamic Long-Only Portfolios
7.2.2 Dynamic Jacobs et al. Long-Short Portfolios
7.2.3 Dynamic Lo-Patel Long-Short Portfolios
7.3 Dynamic Optimization with the Black-Litterman Model
References
Chapter 8: Backtesting
8.1 VaR Tests
8.1.1 Binomial Test
8.1.2 Traffic Light Test
8.1.3 Kupiec´s Tests
8.1.4 Christoffersen´s Tests
8.1.5 Haas´s Tests
8.2 Backtest Results
8.2.1 Historical Optimization
8.2.2 Dynamic Optimizations
References
Chapter 9: Diversification with Real Estate Stocks
Chapter 10: Risk Information and Management
10.1 Early Warning Systems
10.1.1 Chow Test for a Structural Break
10.1.2 Early Warning Based on Tail-Loss Ratio
10.1.3 Early Warning Based on Mahalanobis Distance
10.1.3.1 Copulas
10.1.3.2 Mahalanobis Distance
10.2 Asset Weighting
10.3 Risk Budgets: Incremental and Component Risk
10.3.1 Incremental, Marginal, and Component VaR
10.3.2 Computing VaR, IVaR, MVaR, and ciVaR
10.3.3 Portfolio Results
10.4 Factor Analysis
References
Chapter 11: Optimization with Performance-Attribution Constraints
11.1 Performance-Attribute Constraints
11.2 Application to Domestic REIT Portfolio
References
Chapter 12: Option Pricing
12.1 Double Subordinated Pricing Models
12.2 Option Pricing Under the Double Subordinated IG Model
12.3 Empirical Example
12.3.1 Choice of a and vmax
12.3.2 Option Price and Implied Volatility Surfaces
12.4 Volatility Measures
Appendix 1
Appendix 2
References
Chapter 13: Inclusion of ESG Ratings in Optimization
13.1 REIT ESG Data
13.2 ESG-Valued Returns
13.3 ESG-Valued Optimization
13.4 The ESG Efficient Frontier
13.5 ESG-Valued Tangent Portfolios
13.5.1 Tangent Portfolio Performance over Time
13.6 ESG-Valued Reward-Risk Measures
References
Chapter 14: Inclusion of ESG Ratings in Option Pricing
14.1 Discrete Return Binomial Pricing Model
14.2 ESG-Valued Return Binomial Pricing Model
14.3 ESG-Valued Option Pricing Using a REIT Portfolio as the Underlying
References