A novel hybrid random convolutional kernels model for price volatlity forecasting of precious metals

Abstract

Precious metals are rare metals with high economic value. Forecasting the price volatility of precious metals is essential for investment purposes. In this work, we propose a novel hybrid model of random convolutional kernels-based neural network model (RCK) and generalized autoregressive conditional heteroscedasticity (GARCH) model for forecasting the metal price volatilities of gold, silver, and platinum. Realized volatility calculated on logarithmic returns is used as an estimate for the volatility of prices, and data standardization is performed before feeding the price volatility data to the RCK model. RCK model applies multiple carefully designed random convolution kernels on the time series input to extract robust features for forecasting. The proportion of positive values (PPV) is extracted as features from the output of convolving convolutional kernels with time-series inputs, which are then passed through a regressor to forecast volatility. Compared to the existing methods, the proposed method has the advantage that the weights of the random convolutional kernels need not be trained, unlike other neural network models. Further, no other work has made use of random convolutional kernels for precious metal forecasting, to the best of our knowledge. We incorporated novel learning and data augmentation strategies to achieve better performance. In particular, we used the cosine annealing learning rate strategy and Mixup data augmentation technique to improve the proposed model's performance. We have used MSE (Mean Squared Error), RMSE (Root Mean Squared Error), MAE (Mean Absolute Error), and MAPE (Mean Absolute Percentage Error) as metrics to compare the proposed models' performance. The proposed model decreases the MSE by 53% compared to the GARCH-LSTM model, which is the current state-of-the-art hybrid model for volatility forecasting.