International Journal of Business and Economics Research

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A Study on the Asymmetric Effect to Housing Market Price Volatility

Received: 18 October 2019    Accepted: 20 November 2019    Published: 25 November 2019
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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.

DOI 10.11648/j.ijber.20190806.21
Published in International Journal of Business and Economics Research (Volume 8, Issue 6, December 2019)
Page(s) 406-413
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Housing Price, Volatility, Asymmetric Effect, Information, GJR-GARCH Model

References
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Author Information
  • Department of Real Estate Studies, Namseoul University, Cheonan-Si, Chungnam, Korea

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  • APA Style

    Chasoon Choi. (2019). A Study on the Asymmetric Effect to Housing Market Price Volatility. International Journal of Business and Economics Research, 8(6), 406-413. https://doi.org/10.11648/j.ijber.20190806.21

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    ACS Style

    Chasoon Choi. A Study on the Asymmetric Effect to Housing Market Price Volatility. Int. J. Bus. Econ. Res. 2019, 8(6), 406-413. doi: 10.11648/j.ijber.20190806.21

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    AMA Style

    Chasoon Choi. A Study on the Asymmetric Effect to Housing Market Price Volatility. Int J Bus Econ Res. 2019;8(6):406-413. doi: 10.11648/j.ijber.20190806.21

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  • @article{10.11648/j.ijber.20190806.21,
      author = {Chasoon Choi},
      title = {A Study on the Asymmetric Effect to Housing Market Price Volatility},
      journal = {International Journal of Business and Economics Research},
      volume = {8},
      number = {6},
      pages = {406-413},
      doi = {10.11648/j.ijber.20190806.21},
      url = {https://doi.org/10.11648/j.ijber.20190806.21},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ijber.20190806.21},
      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.},
     year = {2019}
    }
    

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  • TY  - JOUR
    T1  - A Study on the Asymmetric Effect to Housing Market Price Volatility
    AU  - Chasoon Choi
    Y1  - 2019/11/25
    PY  - 2019
    N1  - https://doi.org/10.11648/j.ijber.20190806.21
    DO  - 10.11648/j.ijber.20190806.21
    T2  - International Journal of Business and Economics Research
    JF  - International Journal of Business and Economics Research
    JO  - International Journal of Business and Economics Research
    SP  - 406
    EP  - 413
    PB  - Science Publishing Group
    SN  - 2328-756X
    UR  - https://doi.org/10.11648/j.ijber.20190806.21
    AB  - 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.
    VL  - 8
    IS  - 6
    ER  - 

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