A real-time quantile-regression approach to forecasting gold returns under asymmetric loss
Marian Risse and
Resources Policy, 2015, vol. 45, issue C, 299-306
We propose a real-time quantile-regression approach to analyze whether widely studied macroeconomic and financial variables help to forecast out-of-sample gold returns. The real-time quantile-regression approach accounts for model uncertainty, model instability, and the possibility that a forecaster has an asymmetric loss function. Forecasts are computed and evaluated using the same asymmetric loss function. When the loss function implies that an underestimation is somewhat more costly than an overestimation of the same size, the forecasts computed using the real-time quantile-regression approach outperform forecasts implied by an autoregressive benchmark model.
Keywords: Quantile regression; Forecasting; Asymmetric loss; Gold returns (search for similar items in EconPapers)
JEL-codes: C53 E44 (search for similar items in EconPapers)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:jrpoli:v:45:y:2015:i:c:p:299-306
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