caa a22 4500 445316616 CHVBK 20180317142649.0 cr unu---uuuuu 170323e20110401xx s 000 0 eng 10.1007/s11146-009-9198-7 doi (NATIONALLICENCE)springer-10.1007/s11146-009-9198-7 A Comparison of Alternative Forecast Models of REIT Volatility [Elektronische Daten] [Jian Zhou, Zhixin Kang] 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. Springer Science+Business Media, LLC, 2009 REIT volatility nationallicence Forecasting nationallicence Short-memory model nationallicence Long-memory model nationallicence Zhou Jian Real Estate and Housing, Department of Marketing and Consumer Studies, College of Management and Economics, University of Guelph, N1G 2W1, Guelph, ON, Canada aut Kang Zhixin Department of Economics, Finance, and Decision Sciences, School of Business Administration, University of North Carolina at Pembroke, One University Drive, PO. Box 1510, 28372-1510, Pembroke, NC, USA aut The Journal of Real Estate Finance and Economics Springer US; http://www.springer-ny.com 42/3(2011-04-01), 275-294 0895-5638 42:3<275 2011 42 11146 https://doi.org/10.1007/s11146-009-9198-7 text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11146-009-9198-7 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Zhou Jian Real Estate and Housing, Department of Marketing and Consumer Studies, College of Management and Economics, University of Guelph, N1G 2W1, Guelph, ON, Canada aut NATIONALLICENCE 700 1- Kang Zhixin Department of Economics, Finance, and Decision Sciences, School of Business Administration, University of North Carolina at Pembroke, One University Drive, PO. Box 1510, 28372-1510, Pembroke, NC, USA aut NATIONALLICENCE 773 0- The Journal of Real Estate Finance and Economics Springer US; http://www.springer-ny.com 42/3(2011-04-01), 275-294 0895-5638 42:3<275 2011 42 11146 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer