Forecasting Gold Returns Volatility Over 1258–2023: The Role of Moments

Abstract

We analyze the role of leverage, lower and upper tail risks, skewness, and kurtosis of real gold returns in forecasting its volatility over the annual data sample from 1258 to 2023. To conduct our forecasting experiment, we first fit Bayesian time-varying parameters quantile regressions to real gold returns, under six alternative prior settings, to obtain the estimates of volatility (as inter-quantile range), lower and upper tail risks, skewness, and kurtosis. Second, we forecast the derived estimates of conditional volatility using the information contained in leverage of gold returns, tail risks, skewness, and kurtosis using recursively estimated linear predictive regressions over the out-of-sample periods. We find strong statistical evidence of the role of the moments-based predictors in forecasting gold returns volatility over the short to medium term, i.e., till 1–5-year ahead, when compared to the autoregressive benchmark. Robustness of our main result is also validated based on a shorter sample involving higher-frequency data. Our results have important implications for investors and policymakers.