High-Frequency Volatility Forecasting of US Housing Markets

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 th...

Full description

Saved in:
Bibliographic Details
Published in:The journal of real estate finance and economics 2021-02, Vol.62 (2), p.283-317
Main Authors: Segnon, Mawuli, Gupta, Rangan, Lesame, Keagile, Wohar, Mark E.
Format: Article
Language:English
Subjects:
Economics
Economics and Finance
Financial Services
Forecasting
Housing market
Housing prices
Price indexes
Regional/Spatial Science
Volatility
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c453t-be9dc1327427b7dd06c0ced38a7b4b228d6432731acc8b1f499fe29e78e56b1e3
cites cdi_FETCH-LOGICAL-c453t-be9dc1327427b7dd06c0ced38a7b4b228d6432731acc8b1f499fe29e78e56b1e3
container_end_page 317
container_issue 2
container_start_page 283
container_title The journal of real estate finance and economics
container_volume 62
creator Segnon, Mawuli
Gupta, Rangan
Lesame, Keagile
Wohar, Mark E.
description 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.
doi_str_mv 10.1007/s11146-020-09745-w
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_journals_2485326796</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2485326796</sourcerecordid><originalsourceid>FETCH-LOGICAL-c453t-be9dc1327427b7dd06c0ced38a7b4b228d6432731acc8b1f499fe29e78e56b1e3</originalsourceid><addsrcrecordid>eNp9kE9LAzEQxYMoWKtfwNOC5-jkf4InEWuFigeteAvZbLZurbs12VL67U1dwZunYZj33jx-CJ0TuCQA6ioRQrjEQAGDUVzg7QEaEaEYBi7eDtEItBFYSKaP0UlKSwCQSsMIXU-bxTuexPC1Ca3fFa_dyvXNqul3xaSLwbvUN-2i6Opi_lxMu03ab48ufoQ-naKj2q1SOPudYzSf3L3cTvHs6f7h9maGPResx2UwlSeMKk5VqaoKpAcfKqadKnlJqa4kz1dGnPe6JDU3pg7UBKWDkCUJbIwuhtx17HLN1Ntlt4ltfmkp14JRqYzMKjqofOxSiqG269h8urizBOwekh0g2QzJ_kCy22xigyllcbsI8S_6H9c38c9qNg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2485326796</pqid></control><display><type>article</type><title>High-Frequency Volatility Forecasting of US Housing Markets</title><source>EconLit s plnými texty</source><source>EBSCOhost Business Source Ultimate</source><source>International Bibliography of the Social Sciences (IBSS)</source><source>ABI/INFORM Global</source><source>Springer Link</source><creator>Segnon, Mawuli ; Gupta, Rangan ; Lesame, Keagile ; Wohar, Mark E.</creator><creatorcontrib>Segnon, Mawuli ; Gupta, Rangan ; Lesame, Keagile ; Wohar, Mark E.</creatorcontrib><description>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.</description><identifier>ISSN: 0895-5638</identifier><identifier>EISSN: 1573-045X</identifier><identifier>DOI: 10.1007/s11146-020-09745-w</identifier><language>eng</language><publisher>New York: Springer US</publisher><subject>Economics ; Economics and Finance ; Financial Services ; Forecasting ; Housing market ; Housing prices ; Price indexes ; Regional/Spatial Science ; Volatility</subject><ispartof>The journal of real estate finance and economics, 2021-02, Vol.62 (2), p.283-317</ispartof><rights>Springer Science+Business Media, LLC, part of Springer Nature 2020</rights><rights>Springer Science+Business Media, LLC, part of Springer Nature 2020.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c453t-be9dc1327427b7dd06c0ced38a7b4b228d6432731acc8b1f499fe29e78e56b1e3</citedby><cites>FETCH-LOGICAL-c453t-be9dc1327427b7dd06c0ced38a7b4b228d6432731acc8b1f499fe29e78e56b1e3</cites><orcidid>0000-0002-4967-0609</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2485326796/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2485326796?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,11688,12847,27924,27925,33223,36060,44363,74895</link.rule.ids></links><search><creatorcontrib>Segnon, Mawuli</creatorcontrib><creatorcontrib>Gupta, Rangan</creatorcontrib><creatorcontrib>Lesame, Keagile</creatorcontrib><creatorcontrib>Wohar, Mark E.</creatorcontrib><title>High-Frequency Volatility Forecasting of US Housing Markets</title><title>The journal of real estate finance and economics</title><addtitle>J Real Estate Finan Econ</addtitle><description>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.</description><subject>Economics</subject><subject>Economics and Finance</subject><subject>Financial Services</subject><subject>Forecasting</subject><subject>Housing market</subject><subject>Housing prices</subject><subject>Price indexes</subject><subject>Regional/Spatial Science</subject><subject>Volatility</subject><issn>0895-5638</issn><issn>1573-045X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>8BJ</sourceid><sourceid>M0C</sourceid><recordid>eNp9kE9LAzEQxYMoWKtfwNOC5-jkf4InEWuFigeteAvZbLZurbs12VL67U1dwZunYZj33jx-CJ0TuCQA6ioRQrjEQAGDUVzg7QEaEaEYBi7eDtEItBFYSKaP0UlKSwCQSsMIXU-bxTuexPC1Ca3fFa_dyvXNqul3xaSLwbvUN-2i6Opi_lxMu03ab48ufoQ-naKj2q1SOPudYzSf3L3cTvHs6f7h9maGPResx2UwlSeMKk5VqaoKpAcfKqadKnlJqa4kz1dGnPe6JDU3pg7UBKWDkCUJbIwuhtx17HLN1Ntlt4ltfmkp14JRqYzMKjqofOxSiqG269h8urizBOwekh0g2QzJ_kCy22xigyllcbsI8S_6H9c38c9qNg</recordid><startdate>20210201</startdate><enddate>20210201</enddate><creator>Segnon, Mawuli</creator><creator>Gupta, Rangan</creator><creator>Lesame, Keagile</creator><creator>Wohar, Mark E.</creator><general>Springer US</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>885</scope><scope>8AO</scope><scope>8BJ</scope><scope>8FK</scope><scope>8FL</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ANIOZ</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FQK</scope><scope>FRAZJ</scope><scope>FRNLG</scope><scope>F~G</scope><scope>JBE</scope><scope>K60</scope><scope>K6~</scope><scope>L.-</scope><scope>M0C</scope><scope>M1F</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><orcidid>https://orcid.org/0000-0002-4967-0609</orcidid></search><sort><creationdate>20210201</creationdate><title>High-Frequency Volatility Forecasting of US Housing Markets</title><author>Segnon, Mawuli ; Gupta, Rangan ; Lesame, Keagile ; Wohar, Mark E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c453t-be9dc1327427b7dd06c0ced38a7b4b228d6432731acc8b1f499fe29e78e56b1e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Economics</topic><topic>Economics and Finance</topic><topic>Financial Services</topic><topic>Forecasting</topic><topic>Housing market</topic><topic>Housing prices</topic><topic>Price indexes</topic><topic>Regional/Spatial Science</topic><topic>Volatility</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Segnon, Mawuli</creatorcontrib><creatorcontrib>Gupta, Rangan</creatorcontrib><creatorcontrib>Lesame, Keagile</creatorcontrib><creatorcontrib>Wohar, Mark E.</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection</collection><collection>Banking Information Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>International Bibliography of the Social Sciences (IBSS)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Accounting, Tax &amp; Banking Collection</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>International Bibliography of the Social Sciences</collection><collection>Accounting, Tax &amp; Banking Collection (Alumni)</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>International Bibliography of the Social Sciences</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Global</collection><collection>Banking Information Database</collection><collection>One Business (ProQuest)</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><jtitle>The journal of real estate finance and economics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Segnon, Mawuli</au><au>Gupta, Rangan</au><au>Lesame, Keagile</au><au>Wohar, Mark E.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>High-Frequency Volatility Forecasting of US Housing Markets</atitle><jtitle>The journal of real estate finance and economics</jtitle><stitle>J Real Estate Finan Econ</stitle><date>2021-02-01</date><risdate>2021</risdate><volume>62</volume><issue>2</issue><spage>283</spage><epage>317</epage><pages>283-317</pages><issn>0895-5638</issn><eissn>1573-045X</eissn><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.</abstract><cop>New York</cop><pub>Springer US</pub><doi>10.1007/s11146-020-09745-w</doi><tpages>35</tpages><orcidid>https://orcid.org/0000-0002-4967-0609</orcidid><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 0895-5638
ispartof The journal of real estate finance and economics, 2021-02, Vol.62 (2), p.283-317
issn 0895-5638
1573-045X
language eng
recordid cdi_proquest_journals_2485326796
source EconLit s plnými texty; EBSCOhost Business Source Ultimate; International Bibliography of the Social Sciences (IBSS); ABI/INFORM Global; Springer Link
subjects Economics
Economics and Finance
Financial Services
Forecasting
Housing market
Housing prices
Price indexes
Regional/Spatial Science
Volatility
title High-Frequency Volatility Forecasting of US Housing Markets
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-23T19%3A13%3A57IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=High-Frequency%20Volatility%20Forecasting%20of%20US%20Housing%20Markets&rft.jtitle=The%20journal%20of%20real%20estate%20finance%20and%20economics&rft.au=Segnon,%20Mawuli&rft.date=2021-02-01&rft.volume=62&rft.issue=2&rft.spage=283&rft.epage=317&rft.pages=283-317&rft.issn=0895-5638&rft.eissn=1573-045X&rft_id=info:doi/10.1007/s11146-020-09745-w&rft_dat=%3Cproquest_cross%3E2485326796%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c453t-be9dc1327427b7dd06c0ced38a7b4b228d6432731acc8b1f499fe29e78e56b1e3%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=2485326796&rft_id=info:pmid/&rfr_iscdi=true