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Analyzing the Domestic REIT Market Returns and Price Volatility Using a GARCH-GMDH Hybrid Model

  • Korea Real Estate Review
  • 2025, 35(2), pp.45~60
  • Publisher : korea real estate research institute
  • Research Area : Social Science > Law > Law of Special Parts > Law of Real Estate
  • Received : March 2, 2025
  • Accepted : June 20, 2025
  • Published : June 30, 2025

Jung-Suk Yu 1

1단국대학교

Accredited

ABSTRACT

This study utilizes a hybrid model that combines generalized autoregressive conditional heteroskedasticity (GARCH) with the group method of data handling (GMDH) model to forecast returns and volatility. We focus on the Korean real estate investment trust (REIT) market and compare our hybrid model with the traditional GARCH and GMDH single models. The GARCH (1,1) model effectively captures the volatility clustering phenomenon but cannot fully reflect nonlinear patterns. In contrast, the GMDH model is stronger in handling nonlinearities; therefore, the hybrid model combining GARCH and GMDH is expected to have a higher forecasting performance. This study empirically analyzes Korean REIT market data from the listing date of each stock to the end of December 2024. The results show that our GARCH-GMDH hybrid model outperforms the GARCH and GMDH single models in forecasting returns and volatility, especially in short-term forecasting; the hybrid model can also stably capture extreme volatility and volatility clustering. Analyzing the differences in forecasting performance by market capitalization, trading volume, and time horizon of REITs reveals that REITs with larger market capitalizations and higher trading volumes had higher forecasting accuracy. Conversely, newly listed REITs had higher forecasting accuracy than long-term REITs.

Citation status

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