Making Density Forecasting Models Statistically Consistent

Making Density Forecasting Models Statistically Consistent

We propose a new approach to density forecast optimisation and apply it to Value-at-Risk estimation. All existing density forecasting models try to optimise the distribution of the returns based solely on the predicted density at the observation. In this paper we argue that probabilistic predictions should be optimised on more than just this accuracy score and suggest that the statistical consi...

Evaluating Density Forecasting Models

Density forecasting in regression is gaining popularity as real world applications demand an estimate of the level of uncertainty in predictions. In this paper we describe the two goals of density forecasting sharpness and calibration. We review the evaluation methods available to a density forecaster to assess each of these goals and we introduce a new evaluation method that allows modelers to...

Forecasting Using Consistent Experts

Combining forecasts from different models has shown to perform better than single forecasts in most times series. In this paper new techniques for combining a large number of forecasting models in order to achieve better forecasting performance are introduced. This class of new techniques for combining is based on using consistent experts for forecasting.

Statistically significant forecasting improvements: how much

Testing the out-of-sample forecasting superiority of one model over another requires an a priori partitioning of the data into a model specification /estimation (‘training’) period and a model comparison/evaluation (‘out-of-sample’ or ‘validation’) period. How large a validation period is necessary for a given mean square forecasting error (MSFE) improvement to be statistically significant at t...

The Proposed Mathematical Models For Decision- Making And Forecasting On Euro- Yen In Foreign Exchange Market