High-Frequency Volatility Forecasting of US Housing Markets

Mawuli Segnon (), Rangan Gupta, Keagile Lesame and Mark Wohar
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Mawuli Segnon: Department of Economics, Institute for Econometric and Economic Statistics, University of Münster, Germany
Keagile Lesame: Department of Economics, University of Pretoria, Pretoria, 0002, South Africa

No 201977, Working Papers from University of Pretoria, Department of Economics

Abstract: We propose a logistic smooth transition autoregressive fractionally integrated [STARFI(p,d)] process for modeling and forecasting US housing price volatility. We discuss the statistical properties of the model and investigate its forecasting performance by assuming various specifications for the dynamics underlying the variance process in the model. Using a unique database of daily data on price indices from ten major US cities, and the corresponding daily Composite 10 Housing Price Index, and also a housing futures price index, we find that using the Markov-switching multifractal (MSM) and FIGARCH frameworks for modeling the variance process helps improving the gains in forecast accuracy.

Keywords: US housing prices; GARCH processes; MSM processes; Model confidence set (search for similar items in EconPapers)
JEL-codes: C22 C53 C58 (search for similar items in EconPapers)
Pages: 34 pages
Date: 2019-10
New Economics Papers: this item is included in nep-ets, nep-for, nep-mst, nep-ore, nep-rmg and nep-ure
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Journal Article: High-Frequency Volatility Forecasting of US Housing Markets (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:pre:wpaper:201977

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