| 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. |