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The Real Estate Investment Market: The Current State and Why Advances Are Needed Read chapter Read first chapter

2022 | OriginalPaper | Chapter

7. Dynamic Portfolio Optimization: Beyond MPT

Authors : W. Brent Lindquist, Svetlozar T. Rachev, Yuan Hu, Abootaleb Shirvani

Published in: Advanced REIT Portfolio Optimization

Publisher: Springer International Publishing

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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.

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previous chapter Black–Litterman Optimization Results
next chapter Backtesting
Footnotes
1
To be sure, there are variations of the GARCH model (Bollersley, 1986) designed to include additional behaviors. Some examples are the I(ntegrated)GARCH (Engle & Bollersley, 1986); F(ractionally)I(ntegrated)GARCH (Baillie et al., 1996); E(xponential)GARCH (Nelson, 1991); GARCH-M(ean) (Engle et al., 1986); T(hreshold)GARCH (Zakoian, 1994); and G(losten)J(agannathan)R(unkle)GARCH (Glosten et al., 1993) models.
 
2
In practice, Σ and μ are estimated from sample data. Thus, fitting the distribution tv(x; μ, Σ) to a sample of multivariate data reduces to finding a best-fit value for ν.
 
3
Popular rank correlation tests include Spearman’s ρ, Kendall’s τ, Goodman and Kruskal’s γ, and Somers’ D.
 
4
This is another reason why rank correlation tests are not useful for this data set. The ranking of this subsample of 8 of the 14 items is perfectly correlated from one optimization scheme to the next.
 
Literature
Baillie, R. T., Bollerslev, T., & Mikkelsen, H. O. (1996). Fractionally integrated generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 74(1), 3–30.CrossRef
Bollersley, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(1), 307–327.CrossRef
Engle, R. F. (1982). Autoregressive conditional heteroskedasticity with estimates of the variance of U.K. inflation. Econometrica, 50, 987–1008.CrossRef
Engle, R. F., & Bollersley, T. (1986). Modeling the persistence of conditional variances. Econometric Reviews, 5, 1–50.CrossRef
Engle, R. F., Lilien, D., Robins, R., & R. (1986). Estimating time varying risk premia in the term structure: The ARCH-M model. Econometrica, 55, 391–407.CrossRef
Glosten, L., Jagannathan, R., & Runkle, D. (1993). Relationship between the expected value and volatility of the nominal excess returns on stocks. Journal of Finance, 48, 1779–1802.CrossRef
Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59(2), 347–370.CrossRef
Zakoian, J.-M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18, 931–955.CrossRef
Metadata
Title
Dynamic Portfolio Optimization: Beyond MPT
Authors
W. Brent Lindquist
Svetlozar T. Rachev
Yuan Hu
Abootaleb Shirvani
Copyright Year
2022
Book
Advanced REIT Portfolio Optimization

Print ISBN: 978-3-031-15285-6

Electronic ISBN: 978-3-031-15286-3

Copyright Year: 2022

DOI
https://doi.org/10.1007/978-3-031-15286-3_7