Forecast evaluations for volatile time series :
نویسنده
چکیده
Standard forecasting criteria like the mean square error (MSE) compare point forecasts or a location parameter of the forecasting distribution with actual observations. Such criteria are less suited to comparing forecasts of volatile time series. Therefore we use the average predictive ordinate (APOC) criterion which evaluates the ordinate of the predictive distribution. Using the comparison to a no-change forecasting rule, we suggest taking the RPOC, the ratio of predictive ordinate criteria. We also suggest comparing two volatile forecasts by a decomposition of the squared distances of ordinates into a bias, variance and noise component. The new criteria are demonstrated for stock indices and exchange rates forecasts.
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