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The Role of Copulas in the Housing Crisis

Author

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  • David M. Zimmer

    (Western Kentucky University)

Abstract

Due to its simplicity and familiarity, the Gaussian copula is popular in calculating risk in collaterized debt obligations, but it imposes asymptotic independence such that extreme events appear to be unrelated. This restriction might be innocuous in normal times, but during extreme events, such as the housing crisis, the Gaussian copula might be inappropriate. This paper explores various copula specifications and finds that the degree to which housing prices are related based on the Gaussian copula is too small compared with real housing price data. © 2012 The President and Fellows of Harvard College and the Massachusetts Institute of Technology.

Suggested Citation

  • David M. Zimmer, 2012. "The Role of Copulas in the Housing Crisis," The Review of Economics and Statistics, MIT Press, vol. 94(2), pages 607-620, May.
  • Handle: RePEc:tpr:restat:v:94:y:2012:i:2:p:607-620
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    Citations

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    Cited by:

    1. repec:bla:irvfin:v:17:y:2017:i:1:p:155-162 is not listed on IDEAS
    2. Oh, Dong Hwan & Patton, Andrew J., 2015. "Modelling Dependence in High Dimensions with Factor Copulas," Finance and Economics Discussion Series 2015-51, Board of Governors of the Federal Reserve System (U.S.).
    3. Pérez, Ana & Prieto-Alaiz, Mercedes, 2016. "A note on nonparametric estimation of copula-based multivariate extensions of Spearman’s rho," Statistics & Probability Letters, Elsevier, vol. 112(C), pages 41-50.
    4. Chun-Kei Tsang & Wing-Keung Wong & Ira Horowitz, 2016. "Arbitrage opportunities, efficiency, and the role of risk preferences in the Hong Kong property market," Studies in Economics and Finance, Emerald Group Publishing, vol. 33(4), pages 735-754, October.
    5. David Zimmer, 2015. "Time-Varying Correlation in Housing Prices," The Journal of Real Estate Finance and Economics, Springer, vol. 51(1), pages 86-100, July.
    6. Koirala, Krishna H. & Mishra, Ashok K. & D'Antoni, Jeremy M. & Mehlhorn, Joey E., 2015. "Energy prices and agricultural commodity prices: Testing correlation using copulas method," Energy, Elsevier, vol. 81(C), pages 430-436.
    7. Tsang, Chun-Kei & Wong, Wing-Keung & Horowitz, Ira, 2016. "A stochastic-dominance approach to determining the optimal home-size purchase: The case of Hong Kong," MPRA Paper 69175, University Library of Munich, Germany.
    8. D'Antoni, Jeremy M. & Detre, Joshua D., 2013. "Determining the Nature of Dependency between Agribusiness and Non-Agribusiness Stocks," 2013 Annual Meeting, February 2-5, 2013, Orlando, Florida 143080, Southern Agricultural Economics Association.
    9. Patton, Andrew J., 2012. "A review of copula models for economic time series," Journal of Multivariate Analysis, Elsevier, vol. 110(C), pages 4-18.
    10. Patton, Andrew, 2013. "Copula Methods for Forecasting Multivariate Time Series," Handbook of Economic Forecasting, Elsevier.

    More about this item

    Keywords

    Clayton; Gumbel; CDO; conditional probability; dependence; bubble; contagion;

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G24 - Financial Economics - - Financial Institutions and Services - - - Investment Banking; Venture Capital; Brokerage
    • 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

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