Modelling Returns in US Housing Prices—You’re the One for Me, Fat Tails

Tamás Kiss,Hoang Nguyen,Pär Österholm

doi:10.3390/jrfm14110506

Abstract

In this paper, we analysed the heavy-tailed behaviour in the dynamics of housing-price returns in the United States. We investigated the sources of heavy tails by estimating autoregressive models in which innovations can be subject to GARCH effects and/or non-Gaussianity. Using monthly data from January 1954 to September 2019, the properties of the models were assessed both within- and out-of-sample. We found strong evidence in favour of modelling both GARCH effects and non-Gaussianity. Accounting for these properties improves within-sample performance as well as point and density forecasts.

Highlights

In less than fifteen years, the world has experienced both a global financial crisis and a virus pandemic—both of which have had dire economic consequences
Our aim with the current study was to assess whether non-Gaussianity in housing returns is important when it comes to modelling the dynamics of the series
The best density forecasts are produced by the models with t- or skew-t innovations ( GARCH effects seem to be most relevant for density forecasts)

Summary

Introduction

In less than fifteen years, the world has experienced both a global financial crisis and a virus pandemic—both of which have had dire economic consequences These events have confirmed the fact that large swings in economic variables happen more frequently than what is implied by models based on the traditional assumption of normally distributed disturbances. We primarily aimed to assess what the appropriate distributional features of the innovations are if one aims to model the dynamics of the returns. We studied this issue using different autoregressive (AR) models, where we allowed for different sources of non-Gaussianity. We first conducted within-sample analysis and validated our findings through an out-of-sample forecast exercise

Objectives

Discussion

Conclusion