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!
Description
Summary: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.
ISSN:0895-5638
1573-045X
DOI:10.1007/s11146-020-09745-w