Autoencoder Enhanced Realised GARCH on Volatility Forecasting

TL;DR

This paper introduces an autoencoder-enhanced Realised GARCH model that synthesizes multiple realised volatility measures nonlinearly, improving one-step-ahead volatility forecasting accuracy across major stock markets.

Contribution

It extends existing linear dimension reduction methods by integrating an autoencoder to combine realised volatility measures nonlinearly, enhancing forecasting performance.

Findings

Autoencoder effectively synthesizes volatility measures.

Proposed model outperforms traditional linear methods.

Model shows robustness during COVID-19 period.

Abstract

Realised volatility has become increasingly prominent in volatility forecasting due to its ability to capture intraday price fluctuations. With a growing variety of realised volatility estimators, each with unique advantages and limitations, selecting an optimal estimator may introduce challenges. In this thesis, aiming to synthesise the impact of various realised volatility measures on volatility forecasting, we propose an extension of the Realised GARCH model that incorporates an autoencoder-generated synthetic realised measure, combining the information from multiple realised measures in a nonlinear manner. Our proposed model extends existing linear methods, such as Principal Component Analysis and Independent Component Analysis, to reduce the dimensionality of realised measures. The empirical evaluation, conducted across four major stock markets from January 2000 to June 2022 and including the period of COVID-19, demonstrates both the feasibility of applying an autoencoder to synthesise volatility measures and the superior effectiveness of the proposed model in one-step-ahead rolling volatility forecasting. The model exhibits enhanced flexibility in parameter estimations across each rolling window, outperforming traditional linear approaches. These findings indicate that nonlinear dimension reduction offers further adaptability and flexibility in improving the synthetic realised measure, with promising implications for future volatility forecasting applications.