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Mortgage Default Risks and High-Frequency Predictability of the US Housing Market: A Reconsideration

Mehmet Balcilar, Elie Bouri (), Rangan Gupta and Mark Wohar ()

No 201875, Working Papers from University of Pretoria, Department of Economics

Abstract: Recent evidence, based on a linear framework, tend to suggest that while mortgage default risks can predict weekly and monthly housing returns of the United States, the same does not hold at the daily frequency. We however indicate that, the relationship between daily housing returns with mortgage default risks is in fact nonlinear, and hence a linear predictive model is misspecified. Given this, we use a k-th order nonparametric causality-in-quantiles test, which in turn, allows us to test for predictability over the entire conditional distribution of not only housing returns, but also volatility, by controlling for misspecification due to nonlinearity. Based on this model, we show that mortgage default risks do indeed predict housing returns and volatility, barring at the extreme upper end of the respective conditional distributions.

Keywords: Mortgage Default Risks, Housing Returns and Volatility; Higher-Order Nonparametric Causality in Quantiles Test (search for similar items in EconPapers)
JEL-codes: C22 R30 (search for similar items in EconPapers)
Pages: 9 pages
Date: 2018-11
New Economics Papers: this item is included in nep-rmg and nep-ure
References: View references in EconPapers View complete reference list from CitEc

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