Combining Deep Learning and GARCH Models for Financial Volatility and Risk Forecasting

TL;DR

This paper proposes a hybrid model combining GARCH econometric models with deep learning GRU networks to improve volatility and risk forecasting for financial assets like the S&P 500, gold, and Bitcoin.

Contribution

It introduces a novel hybrid approach integrating GARCH models with GRU neural networks for enhanced financial volatility and risk prediction.

Findings

Hybrid models improve point volatility forecast accuracy.

Hybrid models do not always outperform in VaR and ES risk measures.

Different assets exhibit distinct volatility dynamics affecting model performance.

Abstract

In this paper, we develop a hybrid approach to forecasting the volatility and risk of financial instruments by combining common econometric GARCH time series models with deep learning neural networks. For the latter, we employ Gated Recurrent Unit (GRU) networks, whereas four different specifications are used as the GARCH component: standard GARCH, EGARCH, GJR-GARCH and APARCH. Models are tested using daily logarithmic returns on the S&P 500 index as well as gold price Bitcoin prices, with the three assets representing quite distinct volatility dynamics. As the main volatility estimator, also underlying the target function of our hybrid models, we use the price-range-based Garman-Klass estimator, modified to incorporate the opening and closing prices. Volatility forecasts resulting from the hybrid models are employed to evaluate the assets' risk using the Value-at-Risk (VaR) and Expected Shortfall (ES) at two different tolerance levels of 5% and 1%. Gains from combining the GARCH and GRU approaches are discussed in the contexts of both the volatility and risk forecasts. In general, it can be concluded that the hybrid solutions produce more accurate point volatility forecasts, although it does not necessarily translate into superior VaR and ES forecasts.