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Analyzing Comovements In Housing Prices Using Vine Copulas

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  • David M. Zimmer

Abstract

type="main" xml:id="ecin12156-abs-0001"> Prior to the housing crisis, the Gaussian copula provided the basis for estimates of the degree of diversification of structured mortgage-based securities. The Gaussian copula's popularity stemmed not only from its link to the familiar normal distribution, but also from the fact that, unlike other copula-based models, it readily extends to higher dimensions. But the Gaussian copula has asymptotic independence, such that events, regardless of the strength of their correlation, become independent if one pushes far enough into the tails. Instead, this article forms multivariate models of housing price comovements using vine copulas. These more flexible models not only fit the data better, but they also uncover far stronger correlations between housing price movements, especially during extreme market swings . ( JEL G21, C32, C51)

Suggested Citation

  • David M. Zimmer, 2015. "Analyzing Comovements In Housing Prices Using Vine Copulas," Economic Inquiry, Western Economic Association International, vol. 53(2), pages 1156-1169, April.
    • Handle: RePEc:bla:ecinqu:v:53:y:2015:i:2:p:1156-1169
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    File URL: http://hdl.handle.net/10.1111/ecin.2015.53.issue-2
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    Citations

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    Cited by:

    1. Kang, Sang Hoon & Uddin, Gazi Salah & Ahmed, Ali & Yoon, Seong-Min, 2018. "Multi-scale causality and extreme tail inter-dependence among housing prices," Economic Modelling, Elsevier, vol. 70(C), pages 301-309.
    2. GRIGORIADIS, Vasilis & EMMANOUILIDES, Christos & FOUSEKIS, Panos, 2016. "The Integration Of Pigmeat Markets In The Eu. Evidence From A Regular Mixed Vine Copula," Review of Agricultural and Applied Economics (RAAE), Faculty of Economics and Management, Slovak Agricultural University in Nitra, vol. 19(1), pages 1-10, March.
    3. Lei Hou & Wei Long & Qi Li, 2019. "Comovement of Home Prices: A Conditional Copula Approach," Annals of Economics and Finance, Society for AEF, vol. 20(1), pages 297-318, May.
    4. Kajal Lahiri & Liu Yang, 2023. "Predicting binary outcomes based on the pair-copula construction," Empirical Economics, Springer, vol. 64(6), pages 3089-3119, June.
    5. Andréas Heinen & James B. Kau & Donald C. Keenan & Mi Lim Kim, 2021. "Spatial Dependence in Subprime Mortgage Defaults," The Journal of Real Estate Finance and Economics, Springer, vol. 62(1), pages 1-24, January.
    6. Kjersti Aas, 2016. "Pair-Copula Constructions for Financial Applications: A Review," Econometrics, MDPI, vol. 4(4), pages 1-15, October.
    7. Stelios Bekiros & Amanda Dahlström & Gazi Salah Uddin & Oskar Ege & Ranadeva Jayasekera, 2020. "A tale of two shocks: The dynamics of international real estate markets," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 25(1), pages 3-27, January.
    8. Aristidis K. Nikoloulopoulos & Peter G. Moffatt, 2019. "Coupling Couples With Copulas: Analysis Of Assortative Matching On Risk Attitude," Economic Inquiry, Western Economic Association International, vol. 57(1), pages 654-666, January.
    9. Sukcharoen, Kunlapath & Leatham, David J., 2017. "Hedging downside risk of oil refineries: A vine copula approach," Energy Economics, Elsevier, vol. 66(C), pages 493-507.

    More about this item

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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