Benchmarking Deep Sequential Models on Volatility Predictions for Financial Time Series

TL;DR

This paper empirically evaluates deep sequential models for financial volatility prediction, demonstrating that dilated neural networks outperform traditional and stochastic models on real stock data.

Contribution

It provides an empirical comparison of deep learning architectures for volatility modeling, highlighting the effectiveness of dilated neural networks in financial time series prediction.

Findings

Dilated neural models outperform traditional GARCH and stochastic models.

Deep models show high flexibility and expressive power.

Experiments on 1314 stock series validate the models' accuracy.

Abstract

Volatility is a quantity of measurement for the price movements of stocks or options which indicates the uncertainty within financial markets. As an indicator of the level of risk or the degree of variation, volatility is important to analyse the financial market, and it is taken into consideration in various decision-making processes in financial activities. On the other hand, recent advancement in deep learning techniques has shown strong capabilities in modelling sequential data, such as speech and natural language. In this paper, we empirically study the applicability of the latest deep structures with respect to the volatility modelling problem, through which we aim to provide an empirical guidance for the theoretical analysis of the marriage between deep learning techniques and financial applications in the future. We examine both the traditional approaches and the deep sequential models on the task of volatility prediction, including the most recent variants of convolutional and recurrent networks, such as the dilated architecture. Accordingly, experiments with real-world stock price datasets are performed on a set of 1314 daily stock series for 2018 days of transaction. The evaluation and comparison are based on the negative log likelihood (NLL) of real-world stock price time series. The result shows that the dilated neural models, including dilated CNN and Dilated RNN, produce most accurate estimation and prediction, outperforming various widely-used deterministic models in the GARCH family and several recently proposed stochastic models. In addition, the high flexibility and rich expressive power are validated in this study.