Empirical analysis of long memory and asymmetry effects for the effectiveness of forecasting volatility of returns on the commodity market based on the example of gold and silver

This paper presents an empirical analysis of the significance of the long memory and asymmetry effects for forecasting conditional volatility and market risk on the commodity market based on the example of gold and silver. The analysis involved testing a wide range of linear and non-linear GARCH-type models. The aim of studying dependencies between rates of return and volatility was to select the optimum model. In-sample and out-of-sample analysis indicated that volatility of returns on gold and silver is better described with non-linear volatility models accommodating long memory and asymmetry effects. In particular, the FIAPARCH model proved to be the best for estimating VaR forecasts for long and short trading positions. Also, this model generated the lowest number of violations of Basel II regulations at the confidence level of 99%. Among the models studied, the FIAPARCH has the most elastic news impact curve, which translates into more possibilities to adjust to data. The results of the analyses suggest that within the period studied, the FIAPARCH model was the best predictive tool compared to the other models. This stems from the model’s ability to satisfactorily capture the effects accompanying price volatility of precious metals, i.e. asymmetry and long memory. The FIAPARCH model produced the lowest number of VaR violations (lowest risk of the model) for all series, which means that it seems to be the most advantageous predictive model with respect to gold and silver from the point of view of financial institutions. Attention was also paid to the prevalence and significance of long memory and asymmetry effects, which should be taken into account when using GARCH-class models