Further on the Returns to Non-Traded REITs
By: Joshua Mallett, Craig McCann (May 2022)
Further on the Returns to Non-traded REITs, updates our 2015 paper including 51 additional nontraded REITs that came into existence after May 1, 2015 and either had had a liquidity event or updated their NAVs between May 1, 2015 and December 31, 2019. We documented that returns to nontraded REITs continue to fall substantially short of the returns to traded REITs. For all 140 nontraded REITs, the shortfall relative to traded REITs was at least $59.2 billion. This systematic underperformance was observed for the additional nontraded REITs launched since May 1, 2015 as well as for the nontraded REITs in existence on May 1, 2015. We also documented nontraded REITs' returns were lower than traded REIT returns for capital raised by nontraded REITs in every calendar quarter.
An Empirical Analysis of Non-Traded REITs
By: Brian Henderson, Joshua Mallett, and Craig McCann (Jun 2015)
Published in the Journal of Wealth Management, 19(1):83-94, Summer 2016.
We find that returns to 81 non-traded REITs which had listed, been acquired by or merged with a listed REIT or had updated per share values average 6.3% annually compared to 11.6% returns earned over the same period in traded REITs. A significant portion of non-traded REITs' $45 billion underperformance results from high up?front fees that average 13.2%, and largely compensate brokers. The remainder of the shortfall results from conflicts of interest that permeate the organizational structure of non-traded REITs.
Non-traded REITs that list on a major securities exchange almost always "internalize" their management and administrative functions prior to listing. We observe corresponding reductions in expenses, on average equal to 9.0% of revenues, largely attributable to the elimination of payments to affiliated parties. Institutional ownership of non-traded REITs rarely occurs until after both an exchange listing and the severing of management and advisory functions from the sponsor, consistent with our view that non-traded REIT investors suffer from the lack of monitoring and effective mechanisms for shareholder protection.
Modeling Autocallable Structured Products
By: Geng Deng, Joshua Mallett, and Craig McCann (Mar 2011)
Published in the Journal of Derivatives & Hedge Funds 17, 326-340 (November 2011).
Since first introduced in 2003, the number of autocallable structured products in the U.S. has increased exponentially. The autocall feature immediately converts the product if the reference asset's value rises above a pre-specified call price. Because an autocallable structured product matures immediately if it is called, the autocall feature reduces the product's duration and expected maturity.
In this paper, we present a flexible Partial Differential Equation (PDE) framework to model autocallable structured products. Our framework allows for products with either discrete or continuous autocall dates. We value the autocallable structured products with discrete autocall dates using the finite difference method, and the products with continuous autocall dates using a closed-form solution. In addition, we estimate the probabilities of an autocallable structured-product being called on each call date. We demonstrate our models by valuing a popular autocallable product and quantify the cost to the investor of adding this feature to a structured product.
The Anatomy of Principal Protected Absolute Return Notes
By: Geng Deng, Ilan Guedj, Joshua Mallett, and Craig McCann (Jul 2010)
Published in the Journal of Derivatives, Vol. 19, No. 2, pp. 61-70, 2011.
Principal Protected Absolute Return Barrier Notes (ARBNs) are structured products that guarantee to return the face value of the note at maturity and pay interest if the underlying security's price does not vary excessively.
The SLCG study derives four closed-form valuation approaches which are considered as representative methodologies on valuing structured products. The approaches are: 1) decomposing an ARBN's payoff into double-barrier linear segment options, 2) decomposing an ARBN's payoff into double-barrier call and put options, 3) transforming an ARBN's path-dependent payoff rule into a path-independent payoff rule which significantly simplifies the derivation of product value, and 4) using PDE (Partial Differential Equations) to model an ARBN's payoff and calculate its value. The study shows the four methodologies to value 214 publicly-listed ARBNs issued by six different investment banks. Most of the products are linked to indices such as the S&P 500 Index and the Russell 2000 Index.
The study finds that the ARBNs' fair price is approximately 4.5% below the actual issue price. Each of the ARBN's fair price is stable across all four valuation methodologies.
