The research of reits return and volatility via alternative econometric approaches

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Title:	The research of reits return and volatility via alternative econometric approaches
Other Titles:	應用不同之計量方法研究REITs之報酬與波動性
Authors:	白東岳;Pai, Tung-yueh
Contributors:	淡江大學財務金融學系博士班邱建良;Chiu, Chien-liang
Keywords:	不動產投資信託;狀態轉換;不對稱均衡;波動性預測;REITs;Regime-change;Asymmetric Equilibrium;Volatility forecasting
Date:	2009
Issue Date:	2010-01-11 00:51:08 (UTC+8)
Abstract:	本論文著重於REITs資產的報酬與波動性之衡量與應用，共包含三個部份。第一部份為「不動產投資信託市場的狀態改變:美國市場實證」、第二部份為「EREITs和MREITs之間存在不對稱均衡關係」與第三部份為「GARCH-SGED之REITs波動性預測」。將此三部份的內容簡述如下。第一部分是在研究REITs狀態變化的特性和解釋總體經濟變數對REITs的影響，並探討EREITs和MREITs的風險和報酬之間的差異。本實證分析使用馬可夫模型，並進一步比較常態分配與SGED分配的差異。實證結果顯示，EREITs和MREITs都符合兩狀態轉變過程。特別的是，MREITs的不確定風險高於EREITs而且兩REITs的狀態持續有所差異。此外，EREITs和MREITs對於利率的敏感性也有所不同，EREITs只對長期利率有反應，MREITs則對長短期利率皆有反應。最後，本研究結果顯示兩REITs報酬都呈現高峰厚尾的特性。第二部份使用傳統共整與門檻共整模型，測試EREITs和MREITs長期之間是否存在均衡關係，本研究使用1972年1月到2008年1月的月指數資料進行實證分析，並進一步使用因果關係檢定應用於門檻誤差修正模型，去測試出是否有因果關係存在於EREITs和MREITs。實證結果顯示，EREITs和MREITs存在非對稱均衡關係而且兩REITs互為因果。最後一部分使用GARCH-N, GARCH-ST 和GARCH-SGED模型，探討REITs報酬率分配的設定對樣本外波動性預測績效的影響。實證資料採用REITs的日指數價格，其用意在於進行美國REITs市場之分析，提供投資者討論和比較的論點。實證結果顯示，不論是MSE或MAE作為比較準則，GARCH-SGED 模型在美國市場的波動性預測能力皆優於GARCH-N和GARCH-ST模型。同時，DM檢定統計量進一步證實GARCH-SGED模型顯著優於GARCH-N和GARCH-ST模型。此結果說明了偏態及厚尾特性的分配在波動性預測的重要性。 The purpose of this dissertation is to contribute to the literature on investigating return and volatility of REITs assets which comprises three parts. The first part of the dissertation is entitled “Regime Changes in Real Estate Investment Trusts Markets: Evidence from the United States Market”, the second part is named “Existence of an Asymmetric Equilibrium Relationship between Equity and Mortgage REITs”, and the last one is “REITs Volatility Prediction for Skew-GED Distribution of The GARCH Model”. A brief introduction of these three parts can be summarized as follows: The first part aims to explore the characteristics of regime-changes in REITs, to examine the influence of macroeconomic variables on REITs, and to investigate the differences in the risk and returns of equity REITs and mortgage REITs. The empirical analysis adopts a Markov regime-switching model and further compares the differences under normal and skewed generalized error distributions. Our overall findings show that the two REITs are sensibly modelled as a two-state regime-switching process. In particular, the uncertainty associated with risk mortgage REITs is higher than that for equity REITs, and the regime-persistence varies between the two. Moreover, the sources of interest rate sensitivity for equity and mortgage REITs are found to be different. Equity REITs are only sensitive to long-term interest rates, whereas mortgage REITs are sensitive to both changes in long- and short-term interest rates. Finally, this study shows that the two REITs returns each exhibit the types of height and fat-tails of the density function. Thus, we believe that our approach is methodologically solid and appropriate for providing a better understanding of the effects of regime-changes on the REITs markets. The second part investigates whether a long-run relationship exists between the EREITs and MREITs via traditional and threshold co-integration testing using both monthly indexes running from January 1972 to January 2008. This study further uses Granger-causality tests based on the corresponding threshold error-correction model to assess whether causality exists between the EREITs and MREITs. The empirical results indicate that there is an asymmetric threshold co-integration relationship as well as a bidirectional feedback causality relationship between the EREITs and MREITs. The last part investigates how specification of return distribution for REITs influences the performance of volatility forecasting using three GARCH models (GARCH-N, GARCH-ST and GARCH-SGED). Daily prices on the REIT provide an empirical sample for discussing and comparing relative ability to accurately out-of-sample volatility, given the growth potential of REIT markets in the United State from the perspective of global investors. Empirical results indicate that the GARCH-SGED model is superior to the GARCH-N and GARCH-ST model in forecasting REITs volatility in the United State, for all forecast horizons in which model selection is based on MSE or MAE. Meanwhile, the DM-tests further confirm that volatility forecasts using the GARCH-SGED model are more accurate than those generated using the GARCH-N and GARCH-ST model in all cases. These findings demonstrate the significant influences of both skewness and tail-thickness on the conditional distribution of returns.
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