A Study on the Asymmetric Effect to Housing Market Price Volatility
International Journal of Business and Economics Research
Volume 8, Issue 6, December 2019, Pages: 406-413
Received: Oct. 18, 2019; Accepted: Nov. 20, 2019; Published: Nov. 25, 2019
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Author
Chasoon Choi, Department of Real Estate Studies, Namseoul University, Cheonan-Si, Chungnam, Korea
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Abstract
The objective of this paper empirically analyzes the relations between information and housing market volatility using the housing price index of Seoul, San Francisco and Los Angeles for the time period from January 1995 to July 2019. For the empirical test of the asymmetric effect of information on housing market volatility, this paper employs GJR-GARCH model which enable good information and bad information to have impact on volatility. The analysis results are as follows. First, it was found that the GJR-GARCH (1,1) model is suitable for analyzing the asymmetric reaction of housing price volatility for information types. Second, it was found that for information types, Seoul, San Francisco, and Los Angeles all displayed asymmetric housing price volatility. It was found that Seoul reacted greater to volatility for unexpected positive earnings rate information than unexpected negative earnings rate information, while on the contrary, San Francisco and Los Angeles showed that they reacted greater to unexpected negative earnings rate information than to unexpected positive earnings rate information. These findings support the hypothesis. Third, for sensitivity to volatility, Seoul was found to be about five times higher than San Francisco and Los Angeles. It is necessary to differentiate the housing price volatility prediction model and portfolio composition according to the information type.
Keywords
Housing Price, Volatility, Asymmetric Effect, Information, GJR-GARCH Model
To cite this article
Chasoon Choi, A Study on the Asymmetric Effect to Housing Market Price Volatility, International Journal of Business and Economics Research. Vol. 8, No. 6, 2019, pp. 406-413. doi: 10.11648/j.ijber.20190806.21
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Copyright © 2019 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
References
[1]
Lim, J. M. (2006). A Study on the Volatility of Housing sales Prices. Housing Studies Review, 14 (2), pp. 65-84.
[2]
Kim, M. G. and Jang, K. H. (2003). Financial Economics (2), Seoul: Keyngmoonsa, pp. 201-202.
[3]
Ross, S. A. (1989). Information and Volatility: The No-Arbitrage Martingale Approach to Timing and Resolution Irrelevancy. The Journal of Finance, 44 (1), pp. 1-17.
[4]
Booth, G. G., Chowdhury, M., Martikaien, T. and Tse, Y. (1997). Intraday Volatility in International Stock Index Futures Markets: Meteor Showers or Heat Waves? Management Science, 43, pp. 1564-1576.
[5]
Ng, A. (2000). Volatility spillover effects from Japan and the US to the Pacific- Basin. Journal of International Money and Finance, 19 (2), pp. 207-233.
[6]
Pardo, A. and Torro, H. (2007). Trading with Asymmetric Volatility Spillovers. Journal of Business Finance & Accounting, 34 (9-10), pp. 1548-1568.
[7]
Kim, J. R. and Kim, S. B. (2008). Information Flow Effect Between the Stock Market and the Foreign Exchange Market: An EGARCH Approach. Kukje Kyungje Yongu, 14 (1), pp. 111-135.
[8]
Lee, M. K. and Ohk, K. Y. (2015). Market Anomalies and Multifactor Models: Comparison between the FF Model and the CNZ Model. Asia-Pacific Journal of Financial Studies, 44 (5), pp. 855-885.
[9]
Black, F. (1976). Studies of Stock Price Volatility Changes. Proceedings of the 1976 Meeting of the Business and Economic Statistics Section. American Statistical Association, Washington DC, 177-181.
[10]
French, K. R. Schwert, G. W. and Stambaugh, R. F. (1987). Expected Stock Returns and Volatility. Journal of Financial Economics., 19 (1), pp. 3-29.
[11]
Campbell, J. Y. and Hentschel, L. (1992). No News is Good News: An Asymmetric Model of Changing Volatility in Stock Returns. Journal of Financial Economiccs, 31, pp. 281-318.
[12]
Dolde, W. and Tirtiroglue, D. (2002). Housing Price Volatility Changes and Their Effects. Real Estate Economics, 30 (1), pp. 41-66.
[13]
Crawford, G. W. and Fratantoni, M. C. (2003). Assessing the Forecasting Perfomance of Regime-Switching, ARIMA and GARCH Models of house Prices. Real Estate Economics, 30, pp. 223-243.
[14]
Miller, N. and Peng, L. (2006). Exploring Metropolitan Housing Price Volatility. Journal of Real Estate Finance and Economics, 33 (1), pp. 5-18.
[15]
Miles, W. (2008). Volatility Clustering in U.S. Home Prices. Journal of Real Estate Research, 30 (1), pp. 73-90.
[16]
Lee, C. L. (2009). Housing Price Volatility and its Determinants, Paper submission for presentation at The 15th Pacific Rim Real Estate Society Conference, Sydney, Australia, 18th - 21th January.
[17]
Kim, M. H. (2009). Volatility Spillover Effects in Stock, Bond, and Real Estate Markets. The Association of Business Education, 53, pp. 329-347.
[18]
Willcocks, W. (2010). Conditional Variences in UK Regional House Prices. Spatial Economic Analysis, 5 (3), pp. 339-354.
[19]
Tian, Y. and Gallagher, K. P. (2015). Housing Price Volatility and the Capital Account in China, Boston University Global Economic Gorvernance Institute, Working Paper 2, July, pp. 1-31.
[20]
Lee, K. Y. Lee, J. A. and Jung, J. H. (2015). A comparison of herd behaviors between housing and equity markets - A focus of the assets with large market capitalization -. Korea Real Estate Review, 40, pp. 313-329.
[21]
Lee, M. K. and Lee, S, K. (2016). An Examination on Asymmetric Volatility of Firm Size Stock Indices. The Korea Contents Association, 16 (8), pp. 387-393.
[22]
Kim, B. J. and Lee, C. S. (2016). A Test on the Mutual Impacts between REITs and Stock Market in US. School Paper, 66, pp. 103-115.
[23]
Fernández, R. (2017). Spillover effects and city development. https://editorialexpress.com/cgi-bin/.
[24]
Park, Y. J. and Kim, K. H. (2017). Housing Price Co-movements and Volatility Spillovers in Seoul Metropolitan Area. School Paper, 69, pp. 131-145.
[25]
Glosten, L. Jagannathan, R. and Runkle, D. E. (1989). Relationship between the Expected Value and the Volatility of the Norminal Excess Return on Stocks. Working Paper, Department of Finance, Columbia University.
[26]
Engle, R. F. and Ng, V. K. (1993). Measuring and Testing the Impact of News on Volatility. Journal of Finance, 48, pp. 1749-1778.
[27]
Berndt, E. K. Hall, B. H. Hall, R. E. and Hausman, J. A. (1974). Estimation and Inference in Nonlinear Structural Models. Annals of Economic and Social Measurement. the National Bureau of Economic Research, 3 (4), pp. 653-665.
[28]
Bollerslev, T. (1986). Generalized Autoregressive Conditional Heteroscedasticity. Journal of Econometrics, 31 (3), pp. 307-327.
[29]
Engle, R. F. (1982). Autoregressive Conditional Heteroskedasticity with Estimates of the Variance of U.K. Inflation. Econometrica, 50 (4), pp. 987-1008.
[30]
Dickey, D. and Fuller, W. A. (1979). Distribution of Estimates for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association, 74, pp. 427-431.
[31]
Phillips, P. C. and Perron, P. (1988). Testing for a Unit Root in Time Series Regression. Biometric, 75, pp. 335-346.
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