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Out-of-sample Forecasting (out-of-sample + forecasting)

Distribution by Scientific Domains
Business, Economics, Finance and Accounting100%

Terms modified by Out-of-sample Forecasting

  • out-of-sample forecasting performance
    		•

    Selected Abstracts

    Does the Yield Spread Predict Recessions in the Euro Area?,

    INTERNATIONAL FINANCE, Issue 2 2005
    Fabio MonetaArticle first published online: 16 DEC 200
    This paper studies the informational content of the slope of the yield curve as a predictor of recessions in the euro area and provides evidence of the potential usefulness of this indicator for monetary policy purposes. In particular, the historical predictive power of ten variations of yield spreads, for different segments of the yield curve, is tested using a probit model. The yield spread between the ten-year government bond rate and the three-month interbank rate outperforms all other spreads in predicting recessions in the euro area. The forecast accuracy of the spread between ten-year and three-month interest rates is also explored in an exercise of out-of-sample forecasting. This yield spread appears to contain information beyond that already available in the history of output, and to outperform other competitor indicators. [source]

    A New-Keynesian DSGE model for forecasting the South African economy

    JOURNAL OF FORECASTING, Issue 5 2009
    Dave' Liu, Guangling
    Abstract This paper develops a New-Keynesian Dynamic Stochastic General Equilibrium (NKDSGE) model for forecasting the growth rate of output, inflation, and the nominal short-term interest rate (91 days Treasury Bill rate) for the South African economy. The model is estimated via maximum likelihood technique for quarterly data over the period of 1970:1,2000:4. Based on a recursive estimation using the Kalman filter algorithm, out-of-sample forecasts from the NKDSGE model are compared with forecasts generated from the classical and Bayesian variants of vector autoregression (VAR) models for the period 2001:1,2006:4. The results indicate that in terms of out-of-sample forecasting, the NKDSGE model outperforms both the classical and Bayesian VARs for inflation, but not for output growth and nominal short-term interest rate. However, differences in RMSEs are not significant across the models. Copyright © 2008 John Wiley & Sons, Ltd. [source]

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

    REAL ESTATE ECONOMICS, Issue 2 2003
    Gordon W. Crawford
    While price changes on any particular home are difficult to predict, aggregate home price changes are forecastable. In this context, this paper compares the forecasting performance of three types of univariate time series models: ARIMA, GARCH and regime-switching. The underlying intuition behind regime-switching models is that the series of interest behaves differently depending on the realization of an unobservable regime variable. Regime-switching models are a compelling choice for real estate markets that have historically displayed boom and bust cycles. However, we find that, while regime-switching models can perform better in-sample, simple ARIMA models generally perform better in out-of-sample forecasting. [source]

    An empirical investigation of the GARCH option pricing model: Hedging performance

    THE JOURNAL OF FUTURES MARKETS, Issue 12 2003
    Haynes H. M. Yung
    In this article, we study the empirical performance of the GARCH option pricing model relative to the ad hoc Black-Scholes (BS) model of Dumas, Fleming, and Whaley. Specifically, we investigate the empirical performance of the option pricing model based on the exponential GARCH (EGARCH) process of Nelson. Using S&P 500 options data, we find that the EGARCH model performs better than the ad hoc BS model both in terms of in-sample valuation and out-of-sample forecasting. However, the superiority of out-of-sample performance EGARCH model over the ad hoc BS model is small and insignificant except in the case of deep-out-of-money put options. The out-performance diminishes as one lengthens the forecasting horizon. Interestingly, we find that the more complicated EGARCH model performs worse than the ad hoc BS model in hedging, irrespective of moneyness categories and hedging horizons. For at-the-money and out-of-the-money put options, the underperformance of the EGARCH model in hedging is statistically significant. © 2003 Wiley Periodicals, Inc. Jrl Fut Mark 23:1191,1207, 2003 [source]