Abstract
PurposeThe purpose of this paper is to compare different models’ performance in modelling and forecasting the Finnish house price returns and volatility.Design/methodology/approachThe competing models are the autoregressive moving average (ARMA) model and autoregressive fractional integrated moving average (ARFIMA) model for house price returns. For house price volatility, the exponential generalized autoregressive conditional heteroscedasticity (EGARCH) model is competing with the fractional integrated GARCH (FIGARCH) and component GARCH (CGARCH) models.FindingsResults reveal that, for modelling Finnish house price returns, the data set under study drives the performance of ARMA or ARFIMA model. The EGARCH model stands as the leading model for Finnish house price volatility modelling. The long memory models (ARFIMA, CGARCH and FIGARCH) provide superior out-of-sample forecasts for house price returns and volatility; they outperform their short memory counterparts in most regions. Additionally, the models’ in-sample fit performances vary from region to region, while in some areas, the models manifest a geographical pattern in their out-of-sample forecasting performances.Research limitations/implicationsThe research results have vital implications, namely, portfolio allocation, investment risk assessment and decision-making.Originality/valueTo the best of the author’s knowledge, for Finland, there has yet to be empirical forecasting of either house price returns or/and volatility. Therefore, this study aims to bridge that gap by comparing different models’ performance in modelling, as well as forecasting the house price returns and volatility of the studied market.
Highlights
Forecasting house price returns and volatility is vital for numerous sectors such as consumers, policymakers, investors and risk managers
The models’ in-sample fit performances vary from region to region, while in some areas, the models manifest a geographical pattern in their out-of-sample forecasting performances
Thereafter, the current study extends this methodology by examining the autoregressive moving average (ARMA) and autoregressive fractional integrated moving average (ARFIMA) models’ forecasting performances for cities and sub-areas with no substantial Autoregressive Conditional Heteroscedasticity (ARCH) effects
Summary
Forecasting house price returns and volatility is vital for numerous sectors such as consumers, policymakers, investors and risk managers. After testing for ARCH effects, the former article found grounds of long-range dependence in the house price returns and volatility for a greater number of the Finnish cities and sub-areas. The studies use various Generalised Autoregressive Conditional Heteroscedasticity (GARCH)-type models to investigate house price returns and volatility dynamics.
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