Generalised density forecast combinations

Kapetanios, George; Mitchell, James; Price, Simon; Fawcett, Nicholas

doi:10.1016/j.jeconom.2015.02.047

Cited by 70 publications

(67 citation statements)

References 28 publications

(29 reference statements)

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“…Closely related to our approach is the work of Kapetanios, Mitchell, Price, and Fawcett (2015), who generalize the approach of Hall and Mitchell (2007) and Geweke and Amisano (2011) by proposing nonlinear opinion pools. More specifically, the weights assigned to each density forecast may vary by region of the density.…”

Section: Introductionmentioning

confidence: 99%

Combining density forecasts using focused scoring rules

Opschoor

Dijk

Wel

2017

J of Applied Econometrics

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SummaryWe investigate the added value of combining density forecasts focused on a specific region of support. We develop forecast combination schemes that assign weights to individual predictive densities based on the censored likelihood scoring rule and the continuous ranked probability scoring rule (CRPS) and compare these to weighting schemes based on the log score and the equally weighted scheme. We apply this approach in the context of measuring downside risk in equity markets using recently developed volatility models, including HEAVY, realized GARCH and GAS models, applied to daily returns on the S&P 500, DJIA, FTSE and Nikkei indexes from 2000 until 2013. The results show that combined density forecasts based on optimizing the censored likelihood scoring rule significantly outperform pooling based on equal weights, optimizing the CRPS or log scoring rule. In addition, 99% Value-at-Risk estimates improve when weights are based on the censored likelihood scoring rule.

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Section: Introductionmentioning

confidence: 99%

Combining density forecasts using focused scoring rules

Opschoor

Dijk

Wel

2017

J of Applied Econometrics

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“…More recently, time-varying density combination weights have been considered, showing that they can lead to forecast improvements (see e.g. Waggoner and Zha, 2012;Billio et al, 2013;Kapetanios et al, 2015;Del Negro, Hasegawa and Schorfheide, 2016).…”

Section: Introductionmentioning

confidence: 99%

“…In a somewhat comparable vein, i.e. in order to capture different performances of the models at different quantiles, Kapetanios et al (2015) have considered density combination weights that depend on the region of the distribution.…”

Section: Introductionmentioning

confidence: 99%

Quantile Aggregation of Density Forecasts

Busetti

2017

Oxf Bull Econ Stat

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Quantile aggregation (or ‘Vincentization’) is a simple and intuitive way of combining probability distributions, originally proposed by S.B. Vincent in 1912. In certain cases, such as under Gaussianity, the Vincentized distribution belongs to the same family as that of the individual distributions and it can be obtained by averaging the individual parameters. This article compares the properties of quantile aggregation with those of the forecast combination schemes normally adopted in the econometric forecasting literature, based on linear or logarithmic averages of the individual densities. Analytical results and Monte Carlo experiments indicate that the properties of quantile aggregation are between those of the linear and the logarithmic pool. Larger differences among the combination schemes occur when there are biases in the individual forecasts: in that case quantile aggregation seems preferable on the whole. The practical usefulness of Vincentization is illustrated empirically in the context of linear forecasting models for Italian GDP and quantile predictions of euro area inflation.

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“…The remainder of the paper is organized as follows. Section 2 introduces our beta mixture calibration and combination model and places it in the context of the general density combination approach introduced by Kapetanios et al (2015). This is followed by Section 3, where we propose Bayesian inference based on slice and Gibbs sampling methods.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Nonparametric Calibration and Combination of Predictive Distributions

2015

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We introduce a Bayesian approach to predictive density calibration and combination that accounts for parameter uncertainty and model set incompleteness through the use of random calibration functionals and random combination weights. Building on the work of Ranjan and Gneiting (2010) and Gneiting and Ranjan (2013), we use infinite beta mixtures for the calibration. The proposed Bayesian nonparametric approach takes advantage of the flexibility of Dirichlet process mixtures to achieve any continuous deformation of linearly * This Working Paper should not be reported as representing the views of Norges Bank. The views expressed are those of the authors and do not necessarily reflect those of Norges Bank. We are much indebted to Tilmann Gneiting for helpful discussions and for his contribution to an earlier version of this work. We thank Concepcion Ausin, combined predictive distributions. The inference procedure is based on Gibbs sampling and allows accounting for uncertainty in the number of mixture components, mixture weights, and calibration parameters. The weak posterior consistency of the Bayesian nonparametric calibration is provided under suitable conditions for unknown true density. We study the methodology in simulation examples with fat tails and multimodal densities and apply it to density forecasts of daily S&P returns and daily maximum wind speed at the Frankfurt airport.

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Generalised density forecast combinations

Cited by 70 publications

References 28 publications

Combining density forecasts using focused scoring rules

Combining density forecasts using focused scoring rules

Quantile Aggregation of Density Forecasts

Bayesian Nonparametric Calibration and Combination of Predictive Distributions

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