An adaptive forecasting approach for copper price volatility through hybrid and non-hybrid models

Abstract

This article studies monthly volatility forecasting for the copper market, which is of practical interest for various participants such as producers, consumers, governments, and investors.Using data from 1990 to 2016, we propose a framework composed of a set of time series models such as Auto-Regressive Integrated Moving Average (ARIMA) and Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH), non-parametric models from soft computing, e.g. Artificial Neural Networks (ANN) and Fuzzy Inference Systems (FIS), and hybrid specifications of both. The adaptability characteristic of these models in exogenous variables, their configuration parameters and window size, simultaneously, are provided by a Genetic Algorithm in pursuit of achieving the best possible forecasts. Also, recognized drivers of this specific market are considered.We examine out-of-sample performance based on Heteroskedasticity-adjusted Mean Squared Error (HMSE), and we test model superiority using the Model Confidence Set (MCS). The results show that making forecasts using an adaptive technique is crucial to obtaining robust and improved performance. The Adaptive-GARCH–FIS specification yielded the best forecasting power.