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Forecasting the Volatility of Real Residential Property Prices in Malaysia: A Comparison of Garch Models

The presence of volatility in residential property market prices helps investors generate substantial profit while also causing fear among investors since high volatility implies a high return with a high risk. In a financial time series, volatility refers to the degree to which the residential prop...

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Main Authors: Suleiman, A.A., Othman, M., Daud, H., Abdullah, M.L., Kadir, E.A., Kane, I.L., Husin, A.
Format: Article
Published: Sciendo 2023
Online Access:http://scholars.utp.edu.my/id/eprint/37358/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171682343&doi=10.2478%2fremav-2023-0018&partnerID=40&md5=fa9d9ca4b475c89f15f65841d555f076
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spelling oai:scholars.utp.edu.my:373582023-10-04T08:42:38Z http://scholars.utp.edu.my/id/eprint/37358/ Forecasting the Volatility of Real Residential Property Prices in Malaysia: A Comparison of Garch Models Suleiman, A.A. Othman, M. Daud, H. Abdullah, M.L. Kadir, E.A. Kane, I.L. Husin, A. The presence of volatility in residential property market prices helps investors generate substantial profit while also causing fear among investors since high volatility implies a high return with a high risk. In a financial time series, volatility refers to the degree to which the residential property market price increases or decreases during a particular period. The present study aims to forecast the volatility returns of real residential property prices (RRPP) in Malaysia using three different families of generalized autoregressive conditional heteroskedasticity (GARCH) models. The study compared the standard GARCH, EGARCH, and GJR-GARCH models to determine which model offers a better volatility forecasting ability. The results revealed that the GJR-GARCH (1,1) model is the most suitable to forecast the volatility of the Malaysian RRPP index based on the goodness-of-fit metric. Finally, the volatility forecast using the rolling window shows that the volatility of the quarterly index decreased in the third quarter (Q3) of 2021 and stabilized at the beginning of the first quarter (Q1) of 2023. Therefore, the best time to start investing in the purchase of real residential property in Malaysia would be the first quarter of 2023. The findings of this study can help Malaysian policymakers, developers, and investors understand the high and low volatility periods in the prices of residential properties to make better investment decisions. © 2023 Ahmad Abubakar Suleiman et al., published by Sciendo. Sciendo 2023 Article NonPeerReviewed Suleiman, A.A. and Othman, M. and Daud, H. and Abdullah, M.L. and Kadir, E.A. and Kane, I.L. and Husin, A. (2023) Forecasting the Volatility of Real Residential Property Prices in Malaysia: A Comparison of Garch Models. Real Estate Management and Valuation, 31 (3). pp. 20-31. ISSN 23005289 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171682343&doi=10.2478%2fremav-2023-0018&partnerID=40&md5=fa9d9ca4b475c89f15f65841d555f076 10.2478/remav-2023-0018 10.2478/remav-2023-0018 10.2478/remav-2023-0018
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description The presence of volatility in residential property market prices helps investors generate substantial profit while also causing fear among investors since high volatility implies a high return with a high risk. In a financial time series, volatility refers to the degree to which the residential property market price increases or decreases during a particular period. The present study aims to forecast the volatility returns of real residential property prices (RRPP) in Malaysia using three different families of generalized autoregressive conditional heteroskedasticity (GARCH) models. The study compared the standard GARCH, EGARCH, and GJR-GARCH models to determine which model offers a better volatility forecasting ability. The results revealed that the GJR-GARCH (1,1) model is the most suitable to forecast the volatility of the Malaysian RRPP index based on the goodness-of-fit metric. Finally, the volatility forecast using the rolling window shows that the volatility of the quarterly index decreased in the third quarter (Q3) of 2021 and stabilized at the beginning of the first quarter (Q1) of 2023. Therefore, the best time to start investing in the purchase of real residential property in Malaysia would be the first quarter of 2023. The findings of this study can help Malaysian policymakers, developers, and investors understand the high and low volatility periods in the prices of residential properties to make better investment decisions. © 2023 Ahmad Abubakar Suleiman et al., published by Sciendo.
format Article
author Suleiman, A.A.
Othman, M.
Daud, H.
Abdullah, M.L.
Kadir, E.A.
Kane, I.L.
Husin, A.
spellingShingle Suleiman, A.A.
Othman, M.
Daud, H.
Abdullah, M.L.
Kadir, E.A.
Kane, I.L.
Husin, A.
Forecasting the Volatility of Real Residential Property Prices in Malaysia: A Comparison of Garch Models
author_facet Suleiman, A.A.
Othman, M.
Daud, H.
Abdullah, M.L.
Kadir, E.A.
Kane, I.L.
Husin, A.
author_sort Suleiman, A.A.
title Forecasting the Volatility of Real Residential Property Prices in Malaysia: A Comparison of Garch Models
title_short Forecasting the Volatility of Real Residential Property Prices in Malaysia: A Comparison of Garch Models
title_full Forecasting the Volatility of Real Residential Property Prices in Malaysia: A Comparison of Garch Models
title_fullStr Forecasting the Volatility of Real Residential Property Prices in Malaysia: A Comparison of Garch Models
title_full_unstemmed Forecasting the Volatility of Real Residential Property Prices in Malaysia: A Comparison of Garch Models
title_sort forecasting the volatility of real residential property prices in malaysia: a comparison of garch models
publisher Sciendo
publishDate 2023
url http://scholars.utp.edu.my/id/eprint/37358/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85171682343&doi=10.2478%2fremav-2023-0018&partnerID=40&md5=fa9d9ca4b475c89f15f65841d555f076
_version_ 1779441370655621120
score 13.211869

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