Izvorni znanstveni članak
https://doi.org/10.1080/1331677X.2022.2089192
Volatility forecasting using deep neural network with time-series feature embedding
Wei-Jie Chen
Jing-Jing Yao
Yuan-Hai Shao
Sažetak
Volatility is usually a proxy indicator for market variation or tendency,
containing essential information for investors and policymakers.
This paper proposes a novel hybrid deep neural network
model (HDNN) with temporal embedding for volatility forecasting.
The main idea of our HDNN is that it encodes one-dimensional
time-series data as two-dimensional GAF images, which enables
the follow-up convolution neural network (CNN) to learn volatility-
related feature mappings automatically. Specifically, HDNN
adopts an elegant end-to-end learning paradigm for volatility
forecasting, which consists of feature embedding and regression
components. The feature embedding component explores the
volatility-related temporal information from GAF images via the
elaborate CNN in an underlying temporal embedding space.
Then, the regression component takes these embedding vectors
as input for volatility forecasting tasks. Finally, we examine the
feasibility of HDNN on four synthetic GBM datasets and five realworld
Stock Index datasets in terms of five regression metrics.
The results demonstrate that HDNN has better performance in
most cases than the baseline forecasting models of GARCH,
EGACH, SVR, and NN. It confirms that the volatility-related temporal
features extracted by HDNN indeed improve the forecasting
ability. Furthermore, the Friedman test verifies that HDNN is statistically
superior to the compared forecasting models.
Ključne riječi
Volatility forecasting; timeseries encoding; machine learning; deep neural network; temporal embedding
Hrčak ID:
304170
URI
Datum izdavanja:
31.3.2023.
Posjeta: 696 *