Tamás Kiss (tamas.kiss@oru.se), Hoang Nguyen (hoang.nguyen@oru.se) and Pär Österholm (par.osterholm@oru.se)
Additional contact information
Tamás Kiss: Örebro University School of Business, Postal: Örebro University, School of Business, SE - 701 82 ÖREBRO, Sweden
Hoang Nguyen: Örebro University School of Business, Postal: Örebro University, School of Business, SE - 701 82 ÖREBRO, Sweden
Pär Österholm: Örebro University School of Business, Postal: Örebro University, School of Business, SE - 701 82 ÖREBRO, Sweden
Abstract: In this paper, we analyse the heavy-tailed behaviour in the dynamics of housing-price returns in the United States. We investigate 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 ranging from January 1954 to September 2019, the properties of the models are assessed both within- and out-of-sample. We find 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.
Keywords: Non-Gaussianity; GARCH; Density forecasts; Probability integral transform
JEL-codes: C22; C52; E44; E47; G17
16 pages, October 29, 2020
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