Gold volatility prediction using a CNN-LSTM approach.

• Deep Learning can be applied in financial forecast, improving results of classic models. • Proposed model with architecture based on a CNN-LSTM combination. • Images associated to time series that have static and dynamic information of the data. • CNN-LSTM model to predict the realized volatility for the price of gold. • The proposed model improves the results respect a normal LSTM network and GARCH. Prediction of volatility for different types of financial assets is one of the tasks of greater mathematical complexity in time series prediction, mainly due to its noisy, non-stationary and heteroscedastic structure. On the other hand, gold is an asset of particular importance for hedging and diversification of investment portfolios, and therefore it is important to predict future volatility of this asset. This paper seeks to significantly improve the forecast of gold volatility by combining two deep learning methodologies: short-term memory networks (LSTM) added to convolutional neural networks (specifically a pre-trained VGG16 network). It is important to mention that these types of hybrid architectures have not been used in time series prediction, so it is a completely new approach to solving these types of problems. The CNN-LSTM hybrid model is capable of including images as input which provides a wide variety of information associated with both static and dynamic characteristics of the series. In parallel, different lags of profitability of the series are entered as input, which allows it to learn from the temporal structure. The results show a substantial improvement when this hybrid model is compared to the GARCH and LSTM models. A 37% reduction in MSE is observed compared to the classic GARCH model, and 18% compared to the LSTM model. Finally, the Model Confidence Model (MCS) determines a significant improvement in the prediction of the hybrid model. The fundamental importance of this research lies in the application of a new type of architecture capable of processing various sources of information for any time series prediction task. [ABSTRACT FROM AUTHOR]