COMEX Copper Futures Volatility Forecasting: Econometric Models and Deep Learning

TL;DR

This study compares econometric and deep learning models for forecasting COMEX copper futures volatility, finding that econometric models excel daily, while deep learning models perform better on high-frequency data, with HAR being most effective overall.

Contribution

It provides a comprehensive comparison of econometric and deep learning models for copper futures volatility forecasting across different data frequencies.

Findings

Econometric HAR model outperforms deep learning on daily data.

Deep learning models excel with high-frequency hourly data.

HAR remains most effective for daily volatility forecasting.

Abstract

This paper investigates the forecasting performance of COMEX copper futures realized volatility across various high-frequency intervals using both econometric volatility models and deep learning recurrent neural network models. The econometric models considered are GARCH and HAR, while the deep learning models include RNN (Recurrent Neural Network), LSTM (Long Short-Term Memory), and GRU (Gated Recurrent Unit). In forecasting daily realized volatility for COMEX copper futures with a rolling window approach, the econometric models, particularly HAR, outperform recurrent neural networks overall, with HAR achieving the lowest QLIKE loss function value. However, when the data is replaced with hourly high-frequency realized volatility, the deep learning models outperform the GARCH model, and HAR attains a comparable QLIKE loss function value. Despite the black-box nature of machine learning models, the deep learning models demonstrate superior forecasting performance, surpassing the fixed QLIKE value of HAR in the experiment. Moreover, as the forecast horizon extends for daily realized volatility, deep learning models gradually close the performance gap with the GARCH model in certain loss function metrics. Nonetheless, HAR remains the most effective model overall for daily realized volatility forecasting in copper futures.