Rostamian, Ahoora and O’Hara, John and Jarchi, Delaram (2025) Deep Learning-based VVIX Forecasting with Time Series Image Encoding and Hybrid ResNet-LSTM Model. Computational Economics. DOI https://doi.org/10.1007/s10614-025-11122-9
Rostamian, Ahoora and O’Hara, John and Jarchi, Delaram (2025) Deep Learning-based VVIX Forecasting with Time Series Image Encoding and Hybrid ResNet-LSTM Model. Computational Economics. DOI https://doi.org/10.1007/s10614-025-11122-9
Rostamian, Ahoora and O’Hara, John and Jarchi, Delaram (2025) Deep Learning-based VVIX Forecasting with Time Series Image Encoding and Hybrid ResNet-LSTM Model. Computational Economics. DOI https://doi.org/10.1007/s10614-025-11122-9
Abstract
This paper introduces an innovative approach to predict VVIX (Volatility of VIX) values using a combined image and recurrent pathway model. The dataset spans 2006 to 2023, providing insights into market volatility expectations for S&P 500 options. VVIX is crucial for risk management, portfolio allocation, and trading strategies. The model integrates spatial patterns and temporal dependencies through two pathways. The image pathway converts VVIX data into spatial images using Gramian Angular Fields and Markov Transition Fields and is processed through pre-trained ResNet-18 and convolutional layers. These transformations are suitable for VVIX forecasting as they effectively capture nonlinear temporal dependencies and transition dynamics in volatility data, enabling robust feature extraction for deep learning models. The recurrent pathway captures temporal trends with recurrent layers. Data is preprocessed with varying sliding windows for short-term, mid-term, and long-term sequences. The model is optimised with MSE loss and Adam optimiser, employing a decaying learning rate. Results show mid-term predictions yield balanced accuracy and training time. The proposed ResNet-LSTM model achieves a high coefficient of determination R² of 0.93, demonstrating robust accuracy in predicting VVIX. Further research should explore diverse model architectures, representations, and optimisation strategies, and assess generalisability to varying market conditions and external factors. In conclusion, the proposed model enhances predictive analytics for financial markets, aiding risk management and decision-making with improved VVIX forecasts.
Item Type: | Article |
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Uncontrolled Keywords: | VIX Volatility; Financial Forecasting; Volatility Forecasting; Transfer Learning |
Subjects: | Z Bibliography. Library Science. Information Resources > ZR Rights Retention |
Divisions: | Faculty of Science and Health > Computer Science and Electronic Engineering, School of Faculty of Science and Health > Mathematics, Statistics and Actuarial Science, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 08 Oct 2025 14:42 |
Last Modified: | 08 Oct 2025 14:50 |
URI: | http://repository.essex.ac.uk/id/eprint/41686 |
Available files
Filename: DJ_AR_computational_finance.pdf
Licence: Creative Commons: Attribution 4.0