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Extreme risk measures for REITs: a comparison among alternative methods

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  • Jian Zhou

Abstract

Real Estate Investment Trusts (REITs), traditionally known as an asset of low volatility, have been undergoing a period of unprecedentedly high volatility due to the current financial crisis. This has increased the need to search for appropriate methods to cope with extreme risks. This study aims to meet this need by comparing the performances of several commonly used methods in predicting the conditional Value at Risk (VaR) and Expected Shortfall (ES) for REITs. Our competing methods cover all three broad categories (i.e. nonparametric, parametric and semiparametric) classified by Manganelli and Engle (2004) and display a varying degree of complexity. Overall, our results show that the trio of EGARCH skewed t (EGARCH, Exponential Generalized Autoregressive Conditional Heteroscedacity), GARCH t , and GARCH EVT (EVT, Extreme Value Theory) provide the most reliable forecasts among all methods considered. Their good performance, with only a few exceptions, holds up for a variety of quantiles and is robust to the size of the moving window used to make the forecasts. We also find that GARCH normal and RiskMetrics of J.P. Morgan are the worst performers. Filtered Historical Simulation (FHS) models fall somewhere in between.

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  • Jian Zhou, 2012. "Extreme risk measures for REITs: a comparison among alternative methods," Applied Financial Economics, Taylor & Francis Journals, vol. 22(2), pages 113-126, January.
    • Handle: RePEc:taf:apfiec:v:22:y:2012:i:2:p:113-126
    DOI: 10.1080/09603107.2011.605752
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    1. Chioma Okoro & Marie Mangwi Ayaba, 2023. "Research Trends and Directions on Real Estate Investment Trusts’ Performance Risks," Sustainability, MDPI, vol. 15(6), pages 1-20, March.
    2. Fernanda Maria Müller & Marcelo Brutti Righi, 2018. "Numerical comparison of multivariate models to forecasting risk measures," Risk Management, Palgrave Macmillan, vol. 20(1), pages 29-50, February.
    3. Fahad Almudhaf, 2018. "Backtesting expected shortfall: evidence from European securitized real estate," Applied Economics Letters, Taylor & Francis Journals, vol. 25(3), pages 176-182, February.
    4. Alfonso Novales & Laura Garcia-Jorcano, 2019. "Backtesting Extreme Value Theory models of expected shortfall," Documentos de Trabajo del ICAE 2019-24, Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico.
    5. Righi, Marcelo Brutti & Ceretta, Paulo Sergio, 2015. "A comparison of Expected Shortfall estimation models," Journal of Economics and Business, Elsevier, vol. 78(C), pages 14-47.

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