Qianli Zhao, Chao Wang, Richard Gerlach, Giuseppe Storti, Lingxiang Zhang
arXiv 26 Nov 2024 · Finance — Risk Management
arXiv:2411.17136 · PDF · DOI · OpenAlex · Extracted main text
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.
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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Naimoli, A., Gerlach, R., and Storti, G (2022) Improving the accuracy of tail risk forecasting models by combining several realized volatility estimators self | 1.000 | 10 | 3 | 100% |
| 2 | Hansen, P. R., Huang, Z., and Shek, H. H (2012) Realized GARCH: A joint model for returns and realized measures of volatility | 1.000 | 7 | 4 | 100% |
| 3 | Hinton, G. E. and Salakhutdinov, R. R (2006) Reducing the Dimensionality of Data with Neural Networks | 0.928 | 4 | 3 | 100% |
| 4 | Barndorff-Nielsen, O. E (2004) Power and Bipower Variation with Stochastic Volatility and Jumps | 0.811 | 4 | 2 | 100% |
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| 6 | Andersen, T. G. and Bollerslev, T (1998) Answering the Skeptics: Yes, Standard Volatility Models do Provide Accurate Forecasts | 0.737 | 3 | 2 | 100% |
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| 8 | Hyvarinen, A (1999) Fast and robust fixed-point algorithms for independent component analysis | 0.644 | 2 | 2 | 100% |
| 9 | Tahmasebi, P., Kamrava, S., Bai, T., and Sahimi, M (2020) Machine learning in geo- and environmental sciences: From small to large scale | 0.644 | 2 | 2 | 100% |
| 10 | Tharwat, A (2021) Independent component analysis: An introduction | 0.644 | 2 | 2 | 100% |
Showing the top 10 of 49 scored citations.