High-frequency volatility forecasting of US housing markets

dc.contributor.authorSegnon, Mawuli K.
dc.contributor.authorGupta, Rangan
dc.contributor.authorLesame, Keagile
dc.contributor.authorWohar, Mark E.
dc.date.accessioned2020-05-14T10:07:54Z
dc.date.issued2021-02
dc.description.abstractWe 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.en_ZA
dc.description.departmentEconomicsen_ZA
dc.description.embargo2021-02-17
dc.description.librarianhj2020en_ZA
dc.description.urihttp://link.springer.com/journal/11146en_ZA
dc.identifier.citationSegnon, M., Gupta, R., Lesame, K. et al. High-Frequency Volatility Forecasting of US Housing Markets. Journal of Real Estate Finance and Economics 62, 283–317 (2021). https://doi.org/10.1007/s11146-020-09745-w.en_ZA
dc.identifier.issn0895-5638 (print)
dc.identifier.issn1573-045X (online)
dc.identifier.other10.1007/s11146-020-09745-w
dc.identifier.urihttp://hdl.handle.net/2263/74590
dc.language.isoenen_ZA
dc.publisherSpringeren_ZA
dc.rights© Springer Science+Business Media, LLC, part of Springer Nature 2020. The original publication is available at : http://link.springer.comjournal/11146.en_ZA
dc.subjectModel confidence seten_ZA
dc.subjectMarkov-switching multi-fractal (MSM)en_ZA
dc.subjectUS housing pricesen_ZA
dc.subjectUnited States of America (USA)en_ZA
dc.subjectGARCH processesen_ZA
dc.titleHigh-frequency volatility forecasting of US housing marketsen_ZA
dc.typePostprint Articleen_ZA

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