The role of news in the fluctuations of housing price
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DOIhttp://dx.doi.org/10.21511/imfi.15(3).2018.24
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Article InfoVolume 15 2018, Issue #3, pp. 294-303
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The main purpose of this paper is to evaluate the impact of the news on the housing price volatility in Iran. To do so, symmetric and asymmetric models such as GARCH, T-ARCH, EGARCH and APGARCH are applied by using annual data for the period 1971–2013. The empirical results confirm the asymmetric and leverage effects of news in Iran housing market. Also the impact of shocks indicates that negative news affect the housing price fluctuations further more than positive news with the same size.
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JEL Classification (Paper profile tab)C49, D89, E30, R31
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References25
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Tables7
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Figures4
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- Figure 1. The asymmetric effect of the news on fluctuations
- Figure 2. Distribution of DLHP time series
- Figure 3. Q-Q for disturbing components of selected models of dissimilar conditioned estimated variance
- Figure 4. Actual and estimated values and disruption components in EGARCH model
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- Table 1. Different models of ARCH family in APGARCH model with implied constraints
- Table 2. Stationary test of housing price index
- Table 3. Statistical features of DLHP time series
- Table 4. ARCH-LM test for DLHP
- Table 5. Estimation results of news effects models
- Table 6. Wald test to recognize IGARCH
- Table 7. News effects curve
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- Abunouri, E., Khanalipour, A., & Jafari, A. (2009). The Effect of News on Exchange Rate Volatilities in Iran: An Application of ARCH Family. Iranian Journal of Trade Studies, 50, 101-120.
- Anderson, K., & NG, V. (2002). A Comparison of Predictable Volatility Models Using Option Data (Working Paper). International Monetary Fund.
- Black, F. (1976). Studies of Stock Price Volatility Changes. In Proceedings of the 1976 Meetings of the American Statistical Association, Business and Economic Statistics Section (pp. 177-181).
- Chun, Tsai, I., & Chi Chen, M. (2010). Modeling House Price Volatility States in the UK by Switching ARCH Models. Journal Title Applied Economics, 42(9), 1145-1153.
- Christi, A. (1982). The Stochastic Behavior of Common Stock Variance Value, Leverage and Interest Rate Effect. Journal of Financial Economics, 10, 407-432.
- Bollerslev, T. P. (1986). Generalized Autoregressive Conditional Heteroscedasticity. Journal of Econometrics, 31(3), 307-327.
- Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates for the Variance of United Kingdom Inflation. Econometrica, 50, 987-1007.
- Engle, R., & NG, V. (1993). Measuring and Testing the Impact of News in Volatility. Journal of Finance, 43, 1749-1778.
- Engle, R. (2004). Risk and Volatility: Econometric Models and Financial Practice. American Economic Review, 94(3), 405- 420.
- French, K. R., Schwert, G. W., & Stambaugh, R. (1987). Expected Stock Returns and Volatility. Journal of Financial Economics, 19(1), 3-29.
- Friedmann, R., & Sanddorf- Kohle, W. G. (2002). Volatility Clusterring and Nontrading Days in Chinese Stock Markets. Journal of Economics and Business, 54, 193-217.
- 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. Journal of Finance, 48, 1779-1801.
- Henry, O. (1998). Modeling the Asymmetry of Stock Market Volatility. Applied Financial Economics, 8, 145-153.
- Heidari, H., Feiziyengjeh, S, & Bashiri, S. (2012). An Empirical Investigation of the Effects of Inflation Uncertainty on Economic Growth in Iran. Actual Problems of Economics, 8, 510-520.
- Higgins, M. L., & Bera, A. K. (1992). A Class of Nonlinear ARCH Models. International Economic Review, 33(1), 137-158.
- Hu, J., Su, L., Jin, S., & Jiang, W. (2007). The Rise in House Prices in China: Bubbles or Fundamentals? Economics Bulletin, 3(7), 1-8.
- Li, W., & Wang, S. (2013). Empirical Studies of the effect of Leverage Industry Characteristics. WSEAS Transactions on Business and Economics, 4(10), 306-315.
- Lambertini, L., Mendicino, C., & Punzi, M. T. (2017). Expectations-driven cycles in the housing market. Economic Modelling, 60, 297-312.
- Miles, W. (2008). Volatility Clustering in U.S. Home Prices. American Real Estate Society, 30(1), 73-90.
- Neunkirchen, M., & Lang, H. (2005). Characteristics and Macroeconomic Drivers of House Price Changes in Australia (U21 Global Working Paper).
- Nelson, D. B. (1991). Conditional Heteroscedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370.
- Schwert, G. W. (1989). Why Does Stock Market Volatility Change Over Time? Journal of Finance, 44(5), 1115-1153.
- Zakoian, J. M. (1994). Threshold heteroskedastic models. Journal of Economic Dynamics and Control, 18(5), 931-955.
- Zhang, H., Huang, H., & Hao, H. (2016). Heterogeneous expectation, beliefs evolution and house price volatility. Economic Modelling, 53, 409-418.
- Zhang, Y., Zhang, H., & Seiler, M. J. (2016). The Impact of Information Disclosure on Price Fluctuations and Housing Bubbles: An Experimental Study. Journal of Housing Research, 25(2), 171-193.