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2022 | Buch

Advanced REIT Portfolio Optimization

Innovative Tools for Risk Management

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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

Frontmatter
Chapter 1. The Real Estate Investment Market: The Current State and Why Advances Are Needed

Even more than the Great Recession, the COVID-19 pandemic has severely tested the assumptions on which many real estate models are based. This chapter vividly illustrates this through quotes from financial news publications. These news articles address the potentially irreversible changes in financial and social systems that impact real estate investing, providing the motivation for the central topics addressed in this book: portfolio optimization, backtesting, risk management, option pricing to enable hedging strategies, and the incorporation of ESG considerations into real estate investing. Critically, these topics are presented under a unified rational finance framework, solely in the context of the real estate investment market, using model portfolios of REIT-based assets.

W. Brent Lindquist, Svetlozar T. Rachev, Yuan Hu, Abootaleb Shirvani
Chapter 2. The Data
Abstract
All data sets utilized in the book are discussed in this Chapter. The primary set, used to develop prototype portfolios, consists of 26 domestic REIT ETFs and seven international REITs traded over the counter as ADRs in the USA. These are augmented by five real estate stocks used for portfolio diversification and additional assets representing major stock market classes used to develop factor-analysis models. Benchmark data used to gauge the performance of our prototype portfolios consist of REIT market indices, REIT-based ETFs, a REIT-based mutual fund, and a general market ETF. Price data for all assets was obtained from Bloomberg Professional Services, with the notable exception of data for two of the REIT market indices, which came from the Federal Reserve Bank of St. Louis.
W. Brent Lindquist, Svetlozar T. Rachev, Yuan Hu, Abootaleb Shirvani
Chapter 3. Modern Portfolio Theory
Abstract
The basic elements of modern portfolio theory are covered in this Chapter. Starting from the basics of price return time series, the authors introduce Markowitz’s mean variance optimization and the central concept of the efficient frontier. Extensions to other risk measure optimization methods within the portfolio theory framework are covered, including: tangent portfolio optimization which exploits the relationship between the efficient frontier and the capital market line; minimization of the conditional value-at-risk, a tail-risk measure replacing the variance; and the Black–Litterman model, designed to address issues appearing in mean variance optimization. The classical implementation of these optimization techniques using moving windows of historical asset return data is developed.
W. Brent Lindquist, Svetlozar T. Rachev, Yuan Hu, Abootaleb Shirvani
Chapter 4. Historical Portfolio Optimization: Domestic REITs
Abstract
This chapter introduces a suite of optimized domestic REIT-based portfolios to be considered as models for either REIT-based indices or ETFs. They serve as representative prototypes of strategies implemented by institutional investment managers of actively managed portfolios. The different risk–return profiles presented by the prototype portfolios serve as asset-allocation tools for accommodating various market environments and risk tolerances. Prototypes are developed for optimizations based on mean variance and conditional value-at-risk. Turnover constraints, as a proxy for controlling transaction cost are introduced, as are several reward-to-risk measures. The cumulative price and reward-to-risk measure performance of these prototypes are compared extensively under various strategies, specifically long-only investing, two variations of long–short investing, and momentum investing.
W. Brent Lindquist, Svetlozar T. Rachev, Yuan Hu, Abootaleb Shirvani
Chapter 5. Diversification with International REITs
Abstract
The question of the diversification impact under the inclusion of international REITs into the prototype domestic REIT portfolios of Chap. 4 is addressed in this Chapter. Optimized portfolios consisting solely of the international REITs and of global (combined domestic and international) REITs are constructed and their performance compared with the domestic REIT portfolios of Chap. 4. Overall, whether employing long-only or long–short strategies, there is little evidence that diversification via international REITs can improve performance.
W. Brent Lindquist, Svetlozar T. Rachev, Yuan Hu, Abootaleb Shirvani
Chapter 6. Black–Litterman Optimization Results

The Black–Litterman model was designed to mitigate issues of input sensitivity and estimation error maximization by using a Bayesian approach to incorporate the returns of a market index. It also incorporates the ability to include subjective views based on investment analyst estimates. As subjective views are specific to the market day and the analyst, the exploration of this model in this chapter is restricted to incorporating market equilibrium returns. The performance of Black–Litterman optimized domestic and global REIT portfolios, under long-only investment strategies is compared to the corresponding mean variance optimized counterparts of Chaps. 4 and 5. Reflecting its design, the performance of the Black–Litterman portfolios more closely tracks that of the selected market index than do optimizations that concentrate solely on maximizing the Sharpe ratio.

W. Brent Lindquist, Svetlozar T. Rachev, Yuan Hu, Abootaleb Shirvani
Chapter 7. Dynamic Portfolio Optimization: Beyond MPT
Abstract
Optimization based solely on the REIT returns in a historical time window is severely restricted by that set of realized historical returns, leaving the portfolio vulnerable to downturns unseen in the historical data. Dynamic portfolio optimization, which determines portfolio composition using a massive ensemble of return predictions that are statistically consistent with historical returns but include extreme events safeguard against this vulnerability. Dynamic optimization, based upon ARMA-GARCH models with heavy-tailed innovations and non-Gaussian copulas, is developed in this Chapter for mean variance and conditional value-at-risk measures as well as for the Black–Litterman model. Dynamically optimized portfolios comprised of domestic REITs are computed and their performance compared to corresponding portfolios optimized under the classical historical return approach. Fairly dramatic performance improvement is seen under dynamic optimization.
W. Brent Lindquist, Svetlozar T. Rachev, Yuan Hu, Abootaleb Shirvani
Chapter 8. Backtesting
Abstract
Under regulatory guidelines, banks with substantial trading activity are required to set aside capital to insure against extreme portfolio loss. The size of the capital requirement is determined by the value-at-risk (VaR) of the portfolio. The VaR exposure is determined through a customary set of tests under a procedure referred to as backtesting. This is the subject of this chapter. Eight standard backtests are discussed and applied to historically optimized portfolios of Chaps. 4 and 5 and dynamically optimized portfolios of Chap. 7. Dramatic improvement in the backtests is seen under dynamic optimization. However, improvements in optimization still must be combined with an active management approach incorporating risk-management techniques.
W. Brent Lindquist, Svetlozar T. Rachev, Yuan Hu, Abootaleb Shirvani
Chapter 9. Diversification with Real Estate Stocks
Abstract
Performance of optimized REIT portfolios under diversification via the addition of real estate stocks is considered in this chapter. Under both historical and dynamic optimization, adding the stocks significantly improves the price performance of minimum risk portfolios by reducing the value-at-risk of the portfolios. The reverse holds for the tangent portfolio optimizations, under which adding the stocks dramatically worsens the value-at-risk and consequently the price performance. The reward-to-risk performance measures of the diversified portfolios are compared to the undiversified counterparts. The results of diversification vary with the performance measure but are again generally better for the minimum risk portfolios than for tangent portfolios.
W. Brent Lindquist, Svetlozar T. Rachev, Yuan Hu, Abootaleb Shirvani
Chapter 10. Risk Information and Management
Abstract
This chapter focuses on three risk-management topics: early warning systems; component risk analysis; and factor analysis. An early warning system offers the potential to detect structural breaks in a time series and forecast potential distressed market periods. Each risky asset in a portfolio contributes to the overall risk of a portfolio through the asset’s inherent risk as well as the weight assigned to it. Factor analysis is used to identify external or internal factors that are contributing most strongly to the observed return performance of a portfolio. Two warning systems, component risk analysis techniques and a factor model are developed in this chapter and applied to example REIT portfolios.
W. Brent Lindquist, Svetlozar T. Rachev, Yuan Hu, Abootaleb Shirvani
Chapter 11. Optimization with Performance-Attribution Constraints
Abstract
How well a portfolio performs is of primary concern for investors and governs investor confidence in the portfolio’s management. Attribution analysis provides measures for how well a portfolio is being managed. While performance-attribution measures have been used traditionally as a diagnostic tool, this chapter introduces the recent development to include these measures as constraints in portfolio optimization. Two such measures, asset allocation and the selection effect, are used to constrain conditional value-at-risk optimization of the domestic REIT portfolio under historical and dynamic optimization. The results are analyzed in terms of price and reward-to-risk performance measures. Performance improvement is then characterized in terms of the attribution measure used as the constraint, the optimization method, and the level of turnover constraint.
W. Brent Lindquist, Svetlozar T. Rachev, Yuan Hu, Abootaleb Shirvani
Chapter 12. Option Pricing
Abstract
Call and put options provide a standard tool for hedging exposure to foreseeable risk. The pricing of options is inherently coupled to the price of the underlying asset, hence a model for pricing options must couple innately to the model for the underlying asset price. This chapter details option pricing based upon a so-called double subordination price model for the asset. Subordination models offer the ability to include more of the stylized facts of asset prices, increasing the accuracy of option prices. This chapter details the application of a double subordinated model to capture the mean, variance, skewness, and kurtosis, as well as intrinsic time features of the return process for one of the optimized domestic REIT portfolios.
W. Brent Lindquist, Svetlozar T. Rachev, Yuan Hu, Abootaleb Shirvani
Chapter 13. Inclusion of ESG Ratings in Optimization
Abstract
ESG scores have become the tool to quantify the social responsiveness of asset issuing entities. This chapter develops the framework of ESG-valuation, under which the value of a traded asset is based upon both its financial and ESG components. The relative weighting of the two is controlled by an ESG affinity parameter. Beginning with the concept of an ESG-valued return, application of modern portfolio theory leads to optimization in a three-dimensional space defined by expected ESG-value, an associated risk measure, and ESG score. Efficient frontiers, capital market lines, risk-minimizing and tangent portfolios are definable in this space, leading to optimized, ESG-valued portfolios. This approach is applied to the domestic REIT portfolio using ESG-valued conditional value-at-risk and dynamic optimization. The behavior of its ESG-valued efficient frontiers and the tangent portfolios derived from them are studied as the value of the affinity parameter changes.
W. Brent Lindquist, Svetlozar T. Rachev, Yuan Hu, Abootaleb Shirvani
Chapter 14. Inclusion of ESG Ratings in Option Pricing
Abstract
This chapter develops ESG-valued option pricing to reflect both the financial and ESG worth of the underlying asset. In contrast to Chap. 12, this chapter develops the theory of ESG-valued option pricing using binomial trees employing discrete (rather than continuous) ESG-valued returns. Call option prices are developed using different domestic REIT tangent portfolios as the underlying and their values compared under changes of the ESG affinity parameter. Standard implied volatility surfaces are also derived from the computed call option prices and examined under changing values of the affinity parameter. The discrete option pricing framework enables the incorporation of microeconomic features such as the presence of informed traders, and assessment of option trader views on spot market direction.
W. Brent Lindquist, Svetlozar T. Rachev, Yuan Hu, Abootaleb Shirvani
Metadaten
Titel
Advanced REIT Portfolio Optimization
verfasst von
W. Brent Lindquist
Svetlozar T. Rachev
Yuan Hu
Abootaleb Shirvani
Copyright-Jahr
2022
Electronic ISBN
978-3-031-15286-3
Print ISBN
978-3-031-15285-6
DOI
https://doi.org/10.1007/978-3-031-15286-3