Assessing the Forecasting Performance of Regime‐Switching, ARIMA and GARCH Models of House Prices

Crawford, Gordon; Fratantoni, Michael

doi:10.1111/1540-6229.00064

Cited by 191 publications

(116 citation statements)

References 19 publications

(8 reference statements)

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“…In view of the very good performance of the TARX model both in point and interval predictions, the TARX model can be regarded as the overall winner. This contradicts the results of other studies by Dacco and Satchell (1999), Crawford and Fratantoni (2003) or Bessec and Bouabdallah (2005), where the forecasting performance of regime-switching models was reported to be rather poor.…”

Section: Interval Forecastscontrasting

confidence: 99%

Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models

Misiorek¹,

Trueck²,

Weron³

2006

Studies in Nonlinear Dynamics &Amp; Econometrics

154

151

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In this paper we assess the short-term forecasting power of different time series models in the electricity spot market. In particular we calibrate AR/ARX ("X" stands for exogenous/fundamental variable -system load in our study), AR/ARX-GARCH, TAR/TARX and Markov regime-switching models to California Power Exchange (CalPX) system spot prices. We then use them for out-ofsample point and interval forecasting in normal and extremely volatile periods preceding the market crash in winter 2000/2001. We find evidence that (i) non-linear, threshold regime-switching (TAR/TARX) models outperform their linear counterparts, both in point and interval forecasting, and that (ii) an additional GARCH component generally decreases point forecasting efficiency. Interestingly, the former result challenges a number of previously published studies on the failure of non-linear regime-switching models in forecasting. * The authors are grateful to Dick van Dijk and two anonymous referees for insightful comments and suggestions. This work was supported in part by KBN grant 4-T10B-030-25 (to Misiorek).

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Section: Interval Forecastscontrasting

confidence: 99%

Point and Interval Forecasting of Spot Electricity Prices: Linear vs. Non-Linear Time Series Models

Misiorek¹,

Trueck²,

Weron³

2006

Studies in Nonlinear Dynamics &Amp; Econometrics

154

151

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“…In the U.S., Crawford and Fratantoni (2003) find that regime-switching models fit the data better than ARIMA or GARCH models. In spite of that, the performance of the simpler time-series models turns out to be as good or even better in out-of-sample tests.…”

Section: Literature Reviewmentioning

confidence: 90%

Are Securitized Real Estate Returns More Predictable Than Stock Returns?

Serrano

Hoesli

2008

Book of Abstracts: 15th Annual European Real Estate Society Conference - Krakow, Poland

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This paper examines whether the predictability of securitized real estate returns differs from that of stock returns. It also provides a cross-country comparison of securitized real estate return predictability. In contrast to most of the literature on this issue, the analysis is not based on a multifactor asset pricing framework as such analyses may bias the results. We use a time series approach and thus create a level playing field to compare the predictability of the two asset classes. Forecasts are performed with ARMA and ARMA-EGARCH models and evaluated by comparing the entire empirical distributions of prediction errors, as well as with a trading strategy. The results, based on daily data for the 1990-2007 period, show that securitized real estate returns are generally more predictable than stock returns in countries with mature and well established REIT regimes. ARMA-EGARCH models are found to have portfolio outperformance potential even in the presence of transaction costs, with generally better results for securitized real estate than for stocks.

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“…This paper uses three alternative models to forecast housing supply in the Irish market from the early 90s onwards, namely; a fundamental variable-based Ordinary Least Squares (OLS) model, a Vector Autoregression (VAR) specification and an ARIMA approach. In that sense the paper is similar in spirit to recent comparative forecasting work on house price dynamics such as Crawford and Franatoni (2003) and Guirguis et al (2005). The remainder of the paper is set out as follows.…”

Section: Introductionmentioning

confidence: 94%