A quantile-boosting approach to forecasting gold returns

Does uncertainty move the gold price? New evidence from a nonparametric causality-in-quantiles test

“…Another closely related direction for future research is to explore the implications of the implications of causality-in-quantiles for the predictability of gold-price fluctations. In the recent literature on the predictability of gold-price fluctations, quantiles-based techniques have been studied by Pierdzioch et al (2016). They, however, have not studied whether the uncertainty indexes that we have studied in this research help to forecast gold-price fluctuations.…”

On exchange-rate movements and gold-price fluctuations: evidence for gold-producing countries from a nonparametric causality-in-quantiles test

“…Using data for major gold-producing countries, we have shown that a novel nonparametric causality-in-quantiles test provides new insights into the in-sample causal links between gold-price fluctuations and exchange-rate movements in both their first and second moments. In future research, it is interesting to extend our analysis to a out-of-sample forecasting context, since in-sample predictability does not guarantee the same over the out-ofsample (Bonaccolto et al, 2015; on out-of-sample forecasting of gold-price fluctuations using variants of quantile-regression techniques, see also Pierdzioch et al 2015Pierdzioch et al , 2016.…”

An Efficient Smooth Quantile Boost Algorithm for Binary Classification

“…Motivated by Chen et al [17], we plan to develop the SQBC algorithm in the framework of XGBoost to make the SQBC algorithm more useful. In addition, in [18], the paper applied a quantile-boosting approach to forecast gold returns. The current version of SQBC is only for binary classification problem, and we plan to develop the algorithm to do some other predicting tasks like [18] in the economics and finance.…”