Enhancing Neural Network Volatility Forecasting: Decomposed Volatility Modeling

Abstract

Volatility, as a measure of uncertainty, plays a crucial role in various financial activities such as risk management. Existing methods, including GARCH models and neural network (NN) approaches have their limitations. In this paper, we introduce a novel “decomposed volatility modeling” (DVM) framework, which decomposes the raw volatility signals into two distinct components: the GARCH component and the GARCH Remainder component, which are independently modeled using GARCH and NN techniques. The GARCH component preserves stylized facts signals without distortion by NN, ensuring their retention in the forecast. Simultaneously, the GARCH Remainder, whose stationarity is improved by filtering out the stylized facts signals from the raw volatility series, is modeled using NN. DVM leverages the strengths of both GARCH and NN, which enhances predictive performance. We validate the proposed framework using a comprehensive dataset comprising six financial assets, covering stock indices, cryptocurrencies, and macroeconomic data. The experimental results substantiate that DVM significantly enhances the predictive performance compared with using GARCH or NN model alone, highlighting the efficacy of disentangling the stylized facts signals from the raw volatility series.