[
Akhtar, S., & Khan, N. U. (2016). Modeling volatility on the Karachi Stock Exchange, Pakistan. Journal of Asia Business Studies, 10, 253–275. https://doi.org/10.1108/JABS-05-2015-0060
]Search in Google Scholar
[
Alfeus, M. & Nikitopoulos, C. S. (2022). Forecasting volatility in commodity markets with long-memory models. Journal of Commodity Markets, 100248.
]Search in Google Scholar
[
Alpha Kabine, C. (2022). Determinants of house prices in Malaysia. International Journal of Housing Markets and Analysis, ahead-of-print.
]Search in Google Scholar
[
Auwalu, I., Ahmad Abubakar, S., Usman Aliyu, A. & Suleiman Abubakar, S. (2021). Monitoring Groundwater Quality using Probability Distribution in Gwale, Kano state, Nigeria. Journal of Statistical Modeling & Analytics (JOSMA), 3.
]Search in Google Scholar
[
Balaji, L., Anita, H. B., & Ashok Kumar, B. (2023). Volatility Clustering in Nifty Energy Index Using GARCH Model. In: RAJAKUMAR, G., DU, K.-L., VUPPALAPATI, C. & BELIGIANNIS, G. N., eds. Intelligent Communication Technologies and Virtual Mobile Networks. Singapore. Springer Nature Singapore, 667-681. https://doi.org/10.1007/978-981-19-1844-5_53
]Search in Google Scholar
[
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31, 307–327. https://doi.org/10.1016/0304-4076(86)90063-1
]Search in Google Scholar
[
Bork, L., & Møller, S. V. (2015). Forecasting house prices in the 50 states using Dynamic Model Averaging and Dynamic Model Selection. International Journal of Forecasting, 31, 63–78. https://doi.org/10.1016/j.ijforecast.2014.05.005
]Search in Google Scholar
[
Capelli, P., Ielasi, F., & Russo, A. (2021). Forecasting volatility by integrating financial risk with environmental, social, and governance risk. Corporate Social Responsibility and Environmental Management, 28, 1483–1495. https://doi.org/10.1002/csr.2180
]Search in Google Scholar
[
Crawford, G. W., & Fratantoni, M. C. (2003). Assessing the Forecasting Performance of Regime-Switching, ARIMA and GARCH Models of House Prices. Real Estate Economics, 31, 223–243. https://doi.org/10.1111/1540-6229.00064
]Search in Google Scholar
[
Dai, Z., & Chang, X. (2021). Forecasting stock market volatility: Can the risk aversion measure exert an important role? The North American Journal of Economics and Finance, 58, 101510. https://doi.org/10.1016/j.najef.2021.101510
]Search in Google Scholar
[
Danielsson, J. (2011). Financial risk forecasting: the theory and practice of forecasting market risk with implementation in R and Matlab. John Wiley & Sons.
]Search in Google Scholar
[
Dixit, J. K. & Agrawal, V. (2020). Foresight for stock market volatility – a study in the Indian perspective. Foresight, 22, 1-13.
]Search in Google Scholar
[
Doszyń, M. (2022). Econometric Models of Real Estate Prices with Prior Information. Mixed Estimation. Real Estate Management and Valuation, 30, 61–72. https://doi.org/10.2478/remav-2022-0021
]Search in Google Scholar
[
Dufitinema, J. (2022). Forecasting the Finnish house price returns and volatility: A comparison of time series models. International Journal of Housing Markets and Analysis, 15, 165–187. https://doi.org/10.1108/IJHMA-12-2020-0145
]Search in Google Scholar
[
Enders, W. (2015). Applied econometric time series (4th ed.). Wiley.
]Search in Google Scholar
[
Engle, R. F. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50, 987–1007. https://doi.org/10.2307/1912773
]Search in Google Scholar
[
Fakhfekh, M., & Jeribi, A. (2020). Volatility dynamics of crypto-currencies’ returns: Evidence from asymmetric and long memory GARCH models. Research in International Business and Finance, 51, 101075. https://doi.org/10.1016/j.ribaf.2019.101075
]Search in Google Scholar
[
Gerek, I. H. (2014). House selling price assessment using two different adaptive neuro-fuzzy techniques. Automation in Construction, 41, 33–39. https://doi.org/10.1016/j.autcon.2014.02.002
]Search in Google Scholar
[
Glaeser, E. L., & Nathanson, C. G. (2017). An extrapolative model of house price dynamics. Journal of Financial Economics, 126, 147–170. https://doi.org/10.1016/j.jfineco.2017.06.012
]Search in Google Scholar
[
Glosten, L. R., Jagannathan, R., & Runkle, D. E. (1993). On the relation between the expected value and the volatility of the nominal excess return on stocks. The Journal of Finance, 48, 1779–1801. https://doi.org/10.1111/j.1540-6261.1993.tb05128.x
]Search in Google Scholar
[
Gupta, R., Jurgilas, M., & Kabundi, A. (2010). The effect of monetary policy on real house price growth in South Africa: A factor-augmented vector autoregression (FAVAR) approach. Economic Modelling, 27, 315–323. https://doi.org/10.1016/j.econmod.2009.09.011
]Search in Google Scholar
[
Gupta, R., Kabundi, A., & Miller, S. M. (2011). Forecasting the US real house price index: Structural and non-structural models with and without fundamentals. Economic Modelling, 28, 2013–2021. https://doi.org/10.1016/j.econmod.2011.04.005
]Search in Google Scholar
[
Hameed, A., & Ashraf, H. (2006). Stock market volatility and weak-form efficiency: Evidence from an emerging market [with Comments]. Pakistan Development Review, 45, 1029–1040. https://doi.org/10.30541/v45i4IIpp.1029-1040
]Search in Google Scholar
[
Hanapi, A. L. M., Othman, M., Sokkalingam, R. & Sakidin, H. (2018). Developed a hybrid sliding window and GARCH model for forecasting of crude palm oil prices in Malaysia. Journal of Physics: Conference Series, IOP Publishing, 012029.
]Search in Google Scholar
[
Hong, Y., Wang, L., Liang, C., & Umar, M. (2022). Impact of financial instability on international crude oil volatility: New sight from a regime-switching framework. Resources Policy, 77, 102667. https://doi.org/10.1016/j.resourpol.2022.102667
]Search in Google Scholar
[
Hui, H. C. (2010). House price diffusions across three urban areas in Malaysia. International journal of housing markets and analysis, 4, 369-379.
]Search in Google Scholar
[
Idrees, S. M., Alam, M. A., & Agarwal, P. (2019). A prediction approach for stock market volatility based on time series data. IEEE Access : Practical Innovations, Open Solutions, 7, 17287–17298. https://doi.org/10.1109/ACCESS.2019.2895252
]Search in Google Scholar
[
Kinateder, H., & Wagner, N. (2014). Multiple-period market risk prediction under long memory: When VaR is higher than expected. The Journal of Risk Finance, 15, 4–32. https://doi.org/10.1108/JRF-07-2013-0051
]Search in Google Scholar
[
Kok, S. H., Ismail, N. W. & Lee, C. (2018). The sources of house price changes in Malaysia. International Journal of Housing Markets and Analysis, 11(2), 335-355.
]Search in Google Scholar
[
Kokot, S. (2022). Identification of regularities in relation between prices on primary and secondary housing market in selected cities in Poland. Real Estate Management and Valuation, 30, 45–60. https://doi.org/10.2478/remav-2022-0020
]Search in Google Scholar
[
Koo, E., & Kim, G. (2022). A Hybrid Prediction Model Integrating GARCH Models With a Distribution Manipulation Strategy Based on LSTM Networks for Stock Market Volatility. IEEE Access: Practical Innovations, Open Solutions, 10, 34743–34754. https://doi.org/10.1109/ACCESS.2022.3163723
]Search in Google Scholar
[
Lee, C. L., & Reed, R. G. (2014). The relationship between housing market intervention for first-time buyers and house price volatility. Housing Studies, 29, 1073–1095. https://doi.org/10.1080/02673037.2014.927420
]Search in Google Scholar
[
Liang, C., Li, Y., Ma, F., & Wei, Y. (2021). Global equity market volatilities forecasting: A comparison of leverage effects, jumps, and overnight information. International Review of Financial Analysis, 75, 101750. https://doi.org/10.1016/j.irfa.2021.101750
]Search in Google Scholar
[
Lim, C. M., & Sek, S. K. (2013). Comparing the Performances of GARCH-type Models in Capturing the Stock Market Volatility in Malaysia. Procedia Economics and Finance, 5, 478–487. https://doi.org/10.1016/S2212-5671(13)00056-7
]Search in Google Scholar
[
Liu, H. C., & Hung, J. C. (2010). Forecasting volatility and capturing downside risk of the Taiwanese futures markets under the financial tsunami. Managerial Finance, 36, 860–875. https://doi.org/10.1108/03074351011070233
]Search in Google Scholar
[
Merton, R. C. (1980). On estimating the expected return on the market: An exploratory investigation. Journal of Financial Economics, 8, 323–361. https://doi.org/10.1016/0304-405X(80)90007-0
]Search in Google Scholar
[
Miles, W. (2008). Boom–Bust Cycles and the Forecasting Performance of Linear and Non-Linear Models of House Prices. The Journal of Real Estate Finance and Economics, 36, 249–264. https://doi.org/10.1007/s11146-007-9067-1
]Search in Google Scholar
[
Milunovich, G. (2020). Forecasting Australia’s real house price index: A comparison of time series and machine learning methods. Journal of Forecasting, 39, 1098–1118. https://doi.org/10.1002/for.2678
]Search in Google Scholar
[
Mohammed, G. T., Aduda, J. A., & Kube, A. O. (2020). Improving Forecasts of the EGARCH Model Using Artificial Neural Network and Fuzzy Inference System. Journal of Mathematics, 2020, 1-14. https://doi.org/10.1155/2020/6871396
]Search in Google Scholar
[
Mohd Daud, S. N., & Marzuki, A. (2019). An unobserved component analysis of Malaysia’s house prices. International Journal of Housing Markets and Analysis, 12, 353–376. https://doi.org/10.1108/IJHMA-03-2017-0024
]Search in Google Scholar
[
Nelson, D. B. (1991). Conditional heteroskedasticity in asset returns: A new approach. Econometrica, 59, 347–370. https://doi.org/10.2307/2938260
]Search in Google Scholar
[
Olayemi, M. S., Olubiyi, A. O., Olajide, O. O. & Ajayi, O. F. (2021). Modelling the Efficiency of TGARCH Model in Nigeria Inflation Rate. Journal of Statistical Modeling & Analytics (JOSMA), 3.
]Search in Google Scholar
[
Perlin, M. S., Mastella, M., Vancin, D. F. & Ramos, H. P. (2020). A GARCH Tutorial with R. Revista de Administração Contemporânea, 25.
]Search in Google Scholar
[
Shahid, S., Pour, S. H., Wang, X., Shourav, S. A., Minhans, A., & Ismail, T. (2017). Impacts and adaptation to climate change in Malaysian real estate. International Journal of Climate Change Strategies and Management, 9, 87–103. https://doi.org/10.1108/IJCCSM-01-2016-0001
]Search in Google Scholar
[
Singh, V. V., Suleman, A. A., Ibrahim, A., Abdullahi, U. A., & Suleiman, S. A. (2020). Assessment of probability distributions of groundwater quality data in Gwale area, north-western Nigeria. Annals of Optimization Theory and Practice, 3, 37–46.
]Search in Google Scholar
[
Soon, A. & Tan, C. (2019). An analysis on housing affordability in Malaysian housing markets and the home buyers’ preference. International Journal of Housing Markets and Analysis, 13(3), 375-392. https://doi.org/10.1108/IJHMA-01-2019-0009
]Search in Google Scholar
[
Souza, L., Veiga, A. & Medeiros, M. C. (2002). Evaluating the forecasting performance of GARCH models using White’s Reality Check. Texto para discussão.
]Search in Google Scholar
[
Stock, J. H., & Watson, M. W. (2004). Combination forecasts of output growth in a seven-country data set. Journal of Forecasting, 23, 405–430. https://doi.org/10.1002/for.928
]Search in Google Scholar
[
Tegtmeier, L. (2022). Modeling the volatilities of globally listed private equity markets. Studies in Economics and Finance, ahead-of-print.
]Search in Google Scholar
[
Tsay, R. S. (2005). Analysis of financial time series. John Wiley & Sons. https://doi.org/10.1002/0471746193
]Search in Google Scholar
[
Wang, X., Wen, J., Zhang, Y., & Wang, Y. (2014). Real estate price forecasting based on SVM optimized by PSO. Optik (Stuttgart), 125, 1439–1443. https://doi.org/10.1016/j.ijleo.2013.09.017
]Search in Google Scholar
[
Wang, Y. (2022). Volatility spillovers across NFTs news attention and financial markets. International Review of Financial Analysis, 83, 102313. https://doi.org/10.1016/j.irfa.2022.102313
]Search in Google Scholar
[
Xiao, J., Wen, F., Zhao, Y., & Wang, X. (2021). The role of US implied volatility index in forecasting Chinese stock market volatility: Evidence from HAR models. International Review of Economics & Finance, 74, 311–333. https://doi.org/10.1016/j.iref.2021.03.010
]Search in Google Scholar
[
Xu, X., & Zhang, Y. (2021). House price forecasting with neural networks. Intelligent Systems with Applications, 12, 200052. https://doi.org/10.1016/j.iswa.2021.200052
]Search in Google Scholar
[
Yu, Y., Song, S., Zhou, T., Yachi, H., & Gao, S. (2016). Forecasting house price index of China using dendritic neuron model. International Conference on Progress in Informatics and Computing (PIC), 37-41. https://doi.org/10.1109/PIC.2016.7949463
]Search in Google Scholar
[
Zekri, M. M., & Razali, M. N. (2019). Volatility dynamics of Malaysian listed property companies within the Asian public property markets by using a switching regime approach. Journal of Financial Management of Property and Construction, 25, 5–39. https://doi.org/10.1108/JFMPC-03-2019-0026
]Search in Google Scholar
[
Zhang, Y., Wahab, M. I. M., & Wang, Y. (2022). Forecasting crude oil market volatility using variable selection and common factor. International Journal of Forecasting, 39(1), 486-502.
]Search in Google Scholar
[
Zull Kepili, E. I., & Masron, T. A. (2016). Malaysia property sector. International Journal of Housing Markets and Analysis, 9, 468–482. https://doi.org/10.1108/IJHMA-08-2015-0043
]Search in Google Scholar