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Economic benefits and determinants of extreme dependences between REIT and stock returns

Abstract

The study delivers new implications for risk management and asset allocation by investigating extreme dependences between real estate investment trust (REIT) and stock returns, where ‘extreme dependences’ refer to cross-asset linkages during extraordinary periods. It primarily differentiates itself from prior studies in three respects. First, it examines the role of asymmetric extreme dependences in establishing an optimal investment portfolio during the 2000–2010 period. Second, it provides an economic evaluation of REIT-stock extreme dependences by considering out-of-sample switching fees and break-even transaction costs. Third, it explores the determinants of REIT-stock extreme dependence dynamics during the recent housing boom-and-bust period, which is divided into the housing-boom (pre-break) and housing-bust (post-break) subsamples by the breakpoint of July 31, 2007. The findings demonstrate that the proposed dynamic strategies are superior to a naïve one due to positive break-even transaction costs, and the evaluation results suggest that investors benefit from taking extreme dependences into consideration. It further shows that investors benefit from switching asset holdings from REITs to stocks after the mid-2009, the ending of the recent recession. Except for the illiquidity index, many determinants display weaker explanatory powers of REIT-stock tail dependences in the housing bust than the housing boom.

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Notes

  1. 1.

    For instance, Goetzman and Ibbotson (1990), and Jensen et al. (2000) document that real estate assets are able to hedge against inflation and risks in other asset sectors.

  2. 2.

    The important roles of interest rate term structure and default spread (credit rate spread) in the REIT dynamics are suggested in Lin et al. (2009), Swanson et al. (2002) and Yang et al. (2012). Yang et al. (2012) further document the roles of the mortgage spread and the VIX index in the dynamics of the conditional correlation among stock, bond, and REIT assets.

  3. 3.

    In Cheong et al.’s (2009) discussion on how interest rate changes affect the REIT market, they suggest applying the 10-year Treasury-bill rate as a proxy for the long-term interest rate. For comparison purposes, we consider rates with the three maturities stated above.

  4. 4.

    They propose that REIT-stock dynamic correlations declined from above 60 % in the first sub-period to about 30 % in the second phase after August 1991, rose steadily in the third sub-period from September 2001 onward, and peaked at about 60 % in late 2008.

  5. 5.

    See Longin and Solnik (2001), Poon et al. (2004), and Patton (2006).

  6. 6.

    The appropriate logistic transformation is used to ensure the dependence parameters fall within the interval (−1, 1) in the Gaussian copula and within the interval (0, 1) in the SJC copula.

  7. 7.

    The FTSE REIT index from Datastream is a good representative of the U.S. REIT market and is widely used in the accumulated literature on REIT markets, such as Hoesli and Oikarinen (2012), Yang et al. (2012), Zhou and Anderson (2012), Zhou and Gao (2012), and Zhou (2013), among many others.

  8. 8.

    Case et al. (2012) argue that the CRSP value-weighted Cap-Based Portfolio market index excludes REITs so that it avoids the potential data bias.

  9. 9.

    The Amihud-version illiquidity index is the absolute value of the daily return of the S&P 500 index divided by the daily transaction volume. A high illiquidity index refers to a high liquidity risk because the price moves more than the trade volume.

  10. 10.

    The Michigan Survey of Consumers divides the recent housing boom into two stages: an early boom during 2002–2003 and a second stage during 2004–2005.

  11. 11.

    Several studies show that the accuracy of multi-period-ahead forecasts decays rapidly (e.g., Chou et al. 2009; Maheu and McCurdy 2011). Therefore, this study focuses on the economic evaluation of one-period-ahead forecasts in the dynamic strategies based on copula models through switching fees and break-even transaction costs.

  12. 12.

    Merton (1980) and Connolly et al. (2005) show that, compared with the prediction ability of volatility and dependence structures, there is little evidence that the variation in expected returns can be predicted at the daily level. Thus, this study follows Fleming et al. (2001, 2003) in assuming that the expected returns of REITs and stocks are constant, and focuses on the influence of extreme dependences on the dynamic asset-allocation strategy.

  13. 13.

    The Business Cycle Dating Committee in NBER (the National Bureau of Economic Research) announced that the recent recession started in December 2007 and ended in June 2009.

  14. 14.

    There are primarily three reasons for which this study employs a long/short strategy rather than a long-only strategy. First, this study constructs optimal portfolios based on investors with different risk-averse attitudes and then discusses the economic benefits and diversification strategies of copula-based GARCH models in the REIT and stock markets. If the portfolio allocation is under a long-only strategy, it is possible to construct the same portfolio for different types of investors; it is thus difficult to observe the differences among various investors. Second, the long/short asset allocation sheds more insight into the fluctuations of the portfolio weights of REIT, stock, and risk-free assets than the long-only strategy, and the two strategies are qualitatively the same from the perspective of risk management. Third, in reality, investors can easily construct their portfolio by applying a long/short strategy using related US REIT and stock futures contracts.

  15. 15.

    The naïve portfolio assumes that the REIT and stock returns follow a bivariate normal distribution with unconditional means and unconditional covariance matrices.

  16. 16.

    The naïve portfolio also leads to slight transaction costs in the out-of-sample results, and thus the actual break-even transaction costs based on the selected dynamic strategies should be larger than the estimated values.

  17. 17.

    Yang et al. (2012) show the significant power of factors explaining the linkage between equity REIT and stock assets and between mortgage REIT and stock assets in the post-July 2007.

  18. 18.

    They argue that other stock indices with REITs potentially overestimate the relationship between stock and REIT dynamics.

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Acknowledgments

This research is partially supported by a grant from the National Science Council of Taiwan. The authors are grateful to an anonymous referee and Cheng-Few Lee (the editor) for providing insightful and constructive comments on earlier drafts of this article. All errors and omissions are our responsibility.

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Correspondence to Chih-Chiang Wu.

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Huang, M., Wu, C. Economic benefits and determinants of extreme dependences between REIT and stock returns. Rev Quant Finan Acc 44, 299–327 (2015). https://doi.org/10.1007/s11156-013-0407-3

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Keywords

  • REIT (real estate investment trust)
  • Tail dependence
  • Housing bust
  • Asset allocation strategies

JEL Classification

  • C58
  • G10
  • G11