ANALYZING COMOVEMENTS IN HOUSING PRICES USING VINE COPULAS

Abstract

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)

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