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<p>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&amp;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 R<sup>2</sup> 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.</p>

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Deep Learning-based VVIX Forecasting with Time Series Image Encoding and Hybrid ResNet-LSTM Model

  • 期刊论文

  • 发表日期：

    2025年10月3日

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  • Ahoora Rostamian,
  • John O’Hara,
  • Delaram Jarchi

摘要

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.