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Modelling house price volatility states in Cyprus with switching ARCH models

Author

Listed:
  • Christos S. Savva

    (Department of Commerce, Finance and Shipping, Cyprus University of Technology)

  • Nektarios A. Michail

    (Central Bank of Cyprus)

Abstract

A switching ARCH model is used to estimate the dynamics of the housing market price change volatility in Cyprus during the period 2001q1-2016q2. The results indicate that two states exist: one with high and one with low volatility. Both volatility states exhibit a high degree of persistence. The probability of being in the high volatility state is close to one in the early stages of the sample, and started its decrease when the Cypriot housing boom was peaking around 2008-2010. The findings suggest that booms could be re-enforcing, given the degree of persistence. In addition, higher volatility can be associated with higher credit growth during the period, suggesting that credit expansion can bring more investors to the housing market and increase speculation therein. As overall higher housing volatility increases systemic risk in the economy, the results point out that more regulation would perhaps be advisable.

Suggested Citation

  • Christos S. Savva & Nektarios A. Michail, 2017. "Modelling house price volatility states in Cyprus with switching ARCH models," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 11(1), pages 69-82, June.
  • Handle: RePEc:erc:cypepr:v:11:y:2017:i:1:p:69-82
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    References listed on IDEAS

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    1. William Miles, 2009. "Irreversibility, Uncertainty and Housing Investment," The Journal of Real Estate Finance and Economics, Springer, vol. 38(2), pages 173-182, February.
    2. I-Chun Tsai & Ming-Chi Chen & Tai Ma, 2010. "Modelling house price volatility states in the UK by switching ARCH models," Applied Economics, Taylor & Francis Journals, vol. 42(9), pages 1145-1153.
    3. Case Karl E. & Quigley John M. & Shiller Robert J., 2005. "Comparing Wealth Effects: The Stock Market versus the Housing Market," The B.E. Journal of Macroeconomics, De Gruyter, vol. 5(1), pages 1-34, May.
    4. Bruce Morley & Dennis Thomas, 2011. "Risk-return relationships and asymmetric adjustment in the UK housing market," Applied Financial Economics, Taylor & Francis Journals, vol. 21(10), pages 735-742.
    5. Walter Dolde & Dogan Tirtiroglu, 2002. "Housing Price Volatility Changes and Their Effects," Real Estate Economics, American Real Estate and Urban Economics Association, vol. 30(1), pages 41-66.
    6. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    7. Miles, David, 2012. "Demographics, house prices and mortgage design," Discussion Papers 35, Monetary Policy Committee Unit, Bank of England.
    8. Campbell, Sean D. & Davis, Morris A. & Gallin, Joshua & Martin, Robert F., 2009. "What moves housing markets: A variance decomposition of the rent-price ratio," Journal of Urban Economics, Elsevier, vol. 66(2), pages 90-102, September.
    9. David Miles & Vladimir Pillonca, 2008. "Financial innovation and European housing and mortgage markets," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 24(1), pages 145-175, spring.
    10. Panos Pashardes & Christos S. Savva, 2009. "Factors Affecting House Prices in Cyprus: 1988-2008," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 3(1), pages 3-25, June.
    11. Petros Sivitanides, 2015. "Macroeconomic Influences on Cyprus House Prices: 2006Q1- 2014Q2," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 9(1), pages 3-22, June.
    12. Lamoureux, Christopher G & Lastrapes, William D, 1990. "Persistence in Variance, Structural Change, and the GARCH Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 8(2), pages 225-234, April.
    13. Chyi Lin Lee, 2009. "Housing price volatility and its determinants," International Journal of Housing Markets and Analysis, Emerald Group Publishing Limited, vol. 2(3), pages 293-308, August.
    14. Geoff Willcocks, 2010. "Conditional Variances in UK Regional House Prices," Spatial Economic Analysis, Taylor & Francis Journals, vol. 5(3), pages 339-354.
    15. Hamilton, James D. & Susmel, Raul, 1994. "Autoregressive conditional heteroskedasticity and changes in regime," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 307-333.
    16. Michail Karoglou & Bruce Morley & Dennis Thomas, 2013. "Risk and Structural Instability in US House Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 46(3), pages 424-436, April.
    17. William Miles, 2009. "Housing Investment and the U.S. Economy: How Have the Relationships Changed?," Journal of Real Estate Research, American Real Estate Society, vol. 31(3), pages 329-350.
    18. Norman Miller & Liang Peng, 2006. "Exploring Metropolitan Housing Price Volatility," The Journal of Real Estate Finance and Economics, Springer, vol. 33(1), pages 5-18, August.
    19. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
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    Cited by:

    1. Nektarios A. Michail & George Thucydides, 2018. "Does Housing Wealth Affect Consumption? The Case of Cyprus," Cyprus Economic Policy Review, University of Cyprus, Economics Research Centre, vol. 12(2), pages 67-86, December.
    2. Zhou Jie & Chai Hua Qi, 2023. "An equilibrium analysis of the impact of real estate price volatility on macroeconomics based on ant colony algorithm," Journal of Combinatorial Optimization, Springer, vol. 45(2), pages 1-26, March.
    3. Demetris Koursaros & Nektarios Michail & Niki Papadopoulou & Christos Savva, 2018. "To Create or to Redistribute? That is the Question," Working Papers 2018-4, Central Bank of Cyprus.

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