| Summary: | 碩士 === 中原大學 === 國際商學碩士學位學程 === 102 === This research examines the performance of return and volatility models containing long-memory, asymmetric volatility, and leverage effects by comparing two categories of Real Estate Investment Trusts (REITs) Exchange-traded Funds (ETFs), namely, US REIT ETFs and Global REIT ETFs. This thesis utilizes two short-memory models, the autoregressive moving average – exponential generalized autoregressive conditional heteroskedasticity (ARMA-EGARCH); and autoregressive moving average – asymmetric power autoregressive conditional heteroskedasticity (ARMA-APARCH); and two long-memory models, autoregressive fractionally-integrated moving average – fractionally-integrated exponential generalized autoregressive conditional heteroskedasticity (ARFIMA-FIEGARCH); and autoregressive fractionally-integrated moving average – fractionally-integrated asymmetric power autoregressive conditional heteroskedasticity (ARFIMA-FIAPARCH). The study finds presence of volatility clustering, leverage effects and volatility asymmetry phenomena in both US and Global REIT ETFs. Also, long memory models are better in characterizing future values using lagged returns and volatilities compared to short memory models based on the maximum log-likelihood values. The research also identifies positive long-term dependence in the volatilities of both ETFs, however, fails to conclude dual long memory processes. Nevertheless, the research still can pose a challenge on the weak-form efficient market hypothesis (EMH) of Fama (1970), because historical values of REIT ETFs can still be used to predict their future values. Lastly, US REIT ETFs are seen to be more unstable than their more stationary Global REIT ETFs counterparts. The proper modeling of US and Global REIT ETFs can provide traders, fund managers and investors in creating well-defined trading strategies. Findings can also offer more understanding in the properties of this type of ETFs, and open future channels of research to academicians and researchers.
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