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A Comparison of Alternative Forecast Models of REIT Volatility

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Abstract

This study compares the relative performance of several well-known models in the forecasting of REIT volatility. Overall our results suggest that long-memory models (ARFIMA & FIGARCH) provide the best forecasts. Using either a large sample or some statistically justified small subsamples, we find that long memory models consistently outperform their short-memory counterparts (GARCH & Stochastic Volatility models) over a variety of forecast horizons. We also find that asymmetric models (EGARCH & FIEGARCH) are the worst performers among all models. Our study complements and extends a recent study of Cotter and Stevenson (2008) which demonstrates the usefulness of long-memory models in modeling REIT volatility. We conclude that in addition to modeling REIT volatility, long-memory models should also be adopted to forecast REIT volatility.

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Notes

  1. For instance, in February 2007 the Chicago Board of Trade launched its new futures contract based on the Dow Jones U.S. Real Estate Index (DJUSRE). As of March 31, 2007, the DJUSRE Index included 91 constituents, of which 85 were REITs. The DJUSRE Index futures contract trades electronically six days a week, and has a value equal to 100 multiplied by the value of the DJUSRE.

  2. The distribution of REIT returns is estimated using the Kernel density estimation method.

  3. In this paper, we use the Ox software to estimate the models and perform the forecasting.

  4. The forecast period ends on 04/11/2008. Due to the construction method, the last RV that can be generated is for 04/11/2008, which is 22 days prior to 04/30/2008, the ending date of our data sample.

  5. There are only 246 5-day ahead forecasts (05/04/2007-04/11/2008) because the first forecast we can obtain is for 05/04/2007. It occurs when the rolling window is fixed at the first 2100 observations (01/05/1999 to 04/30/2007). In a similar reasoning, for 10-, 15-, 20-, and 25-day horizons, we generate 241, 236, 231, and 226 forecasts, respectively.

  6. We are indebted to an anonymous reviewer for bringing attention to this point.

  7. Applying this procedure to the historical volatility (HV) also yields two break points—7/10/2002 and 5/11/2006. Compared with the two break points generated from realized volatility (RV), the first one is very close and the second one is actually the same.

  8. Using the three subsamples defined by the two break points generated from the historical volatility (HV), we obtain very similar forecasting results. To conserve space, they are not shown here but are available upon request.

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Authors and Affiliations

  1. Real Estate and Housing, Department of Marketing and Consumer Studies, College of Management and Economics, University of Guelph, Guelph, ON, N1G 2W1, Canada

    Jian Zhou

  2. Department of Economics, Finance, and Decision Sciences, School of Business Administration, University of North Carolina at Pembroke, One University Drive, PO. Box 1510, Pembroke, NC, 28372-1510, USA

    Zhixin Kang

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Zhou, J., Kang, Z. A Comparison of Alternative Forecast Models of REIT Volatility. J Real Estate Finan Econ 42, 275–294 (2011). https://doi.org/10.1007/s11146-009-9198-7

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