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Autoencoder Enhanced Realised GARCH on Volatility Forecasting

Qianli Zhao, Chao Wang, Richard Gerlach, Giuseppe Storti, Lingxiang Zhang

TL;DR

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 to reduce the dimensionality of realised measures is proposed.

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.

Autoencoder Enhanced Realised GARCH on Volatility Forecasting

TL;DR

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 to reduce the dimensionality of realised measures is proposed.

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.

Paper Structure

This paper contains 33 sections, 29 equations, 6 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Research Question and Objective
  4. Thesis Structure
  5. Literature Review
  6. GARCH Models and Realised Volatility
  7. Introduction to GARCH Models
  8. Realised Volatility Measures
  9. Incorporating Realised Measures in GARCH Models
  10. Linear Dimension Reduction
  11. Nonlinear Dimension Reduction
  12. Development of Research Proposal
  13. Methodology
  14. Background Models
  15. GARCH and GARCH-X Models
  16. ...and 18 more sections

Figures (6)

  • Figure 1: Autoencoder model.
  • Figure 2: Absolute returns and the square root of 5-min RV for the full dataset of S&P 500.
  • Figure 3: Absolute returns and the square root of 5-min RV and the square root of synthetic measure from autoencoder in S&P 500 in-sample.
  • Figure 4: Square root of synthetic realised measure from PCA, average and autoencoder in S&P 500 in-sample.
  • Figure 5: Forecast volatility of AE-RealGARCH and AVG-RealGARCH (top), and PC-RealGARCH and IC-RealGARCH (bottom), along with absolute returns in S&P 500 out-of-sample period.
  • ...and 1 more figures