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Extreme Risk Measures for International REIT Markets


Extreme risks associated with extraordinary market conditions are catastrophic for all investors. The ongoing financial crisis has perfectly exemplified this point. Surprisingly, there are few studies exploring this issue for REITs. This study aims to close the knowledge gap. We conduct a comprehensive study by utilizing all three methodological categories to examine their forecasting performances of VaR and ES for nine major global REIT markets. Our findings indicate that there is no universally adequate method to model extreme risks across global markets. Also, estimating risks for the stock and REIT markets may require different methods. In addition, we compare the risk profiles between the stock and REIT markets, and find that the extreme risks for REITs are generally higher than those of stock markets. The fluctuations of risk levels are well synchronized between the two types of markets. The current crisis has significantly increased the extreme risk exposure for both REIT and stock investors. In all, our results have significant implications for REIT risk management, portfolio selection, and evaluation.

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  1. 1.

    We are grateful for an anonymous referee for suggesting to include more countries.

  2. 2.

    Our focus on day-ahead forecast is consistent with the holding period considered for internal risk control by most financial firms. To generate multiple-day risk forecasts, some complicated simulation techniques are needed. This issue is better suited for a follow-up study which is currently being conducted by the authors. However, there do exist some simple ways to make multiple-day forecasts. For instance, the simple scaling method assumes that \( VaR_p^t(h) = VaR_p^t{h^\lambda } \), where \( VaR_p^t(h) \)is the h-day forecast of VaR, \( VaR_p^t \) is the 1-day forecast, and λ is a scaling exponent. However, the validity of this method has been hotly debated in the literature. See Danielsson and de Vries (1997), Diebold et al. (1998) and McNeil and Frey (2000).

  3. 3.

    We thank an anonymous referee for the suggestion of considering the GED distribution.

  4. 4.

    This is defined for the quantiles on the left tail (1% & 5%). For those (95% & 99%) on the right tail, the hit sequence is defined as \( {I_{t + 1}} = \left\{ \begin{gathered} 1,\mathop {}\nolimits if\mathop {}\nolimits {x_{t + 1}} > VaR_p^t \hfill \\0,\mathop {}\nolimits if\mathop {}\nolimits {x_{t + 1}} < VaR_p^t \hfill \\\end{gathered} \right. \).

  5. 5.

    To preserve space, the plots of 1% and 99% VaR forecasts are not presented here but are available upon request.

  6. 6.

    In this table, we choose 07/01/2009 as the cut-off date between the pre-crisis and during-crisis periods. Even though the choice of this date seems arbitrary, it has some merits because moving the cut-off date backwards and forwards several months does not significantly change our results.


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We thank the Editor and anonymous referee for their valuable comments and suggestions. We also thank Joshua Harris and Matthew Hurst for their research assistance. All errors remain our own.

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Correspondence to Jian Zhou.

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Zhou, J., Anderson, R.I. Extreme Risk Measures for International REIT Markets. J Real Estate Finan Econ 45, 152–170 (2012).

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  • Value-at-Risk
  • Expected shortfall
  • Extreme risks
  • Financial crisis
  • REITs