# DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions

arXiv: https://arxiv.org/abs/2210.04797 · HTML: https://mlsanity.com/paper/2210.04797/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 112 references that are in the map.
- First posted on arXiv 23 Sep 2022.

## Builds on (most cited first, 19 of 19)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Pixel Recurrent Neural Networks](https://mlsanity.com/paper/1601.06759/) (2016)
- [Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting](https://mlsanity.com/paper/1907.00235/) (2019)
- [Time Series Forecasting With Deep Learning: A Survey](https://mlsanity.com/paper/2004.13408/) (2020)
- [Conditional Time Series Forecasting with Convolutional Neural Networks](https://mlsanity.com/paper/1703.04691/) (2017)
- [Machine Learning Advances for Time Series Forecasting](https://mlsanity.com/paper/2012.12802/) (2020)
- [Very Deep Convolutional Networks for Text Classification](https://mlsanity.com/paper/1606.01781/) (2016)
- [Multi-Transformer: A New Neural Network-Based Architecture for Forecasting S&P Volatility](https://mlsanity.com/paper/2109.12621/) (2021)
- [Deep Autoregressive Models with Spectral Attention](https://mlsanity.com/paper/2107.05984/) (2021)
- [Graph-Based Learning for Stock Movement Prediction with Textual and Relational Data](https://mlsanity.com/paper/2107.10941/) (2021)
- [Realised Volatility Forecasting: Machine Learning via Financial Word Embedding](https://mlsanity.com/paper/2108.00480/) (2021)
- [Nonstationary Portfolios: Diversification in the Spectral Domain](https://mlsanity.com/paper/2102.00477/) (2021)
- [Dynamic Portfolio Cuts: A Spectral Approach to Graph-Theoretic Diversification](https://mlsanity.com/paper/2106.03417/) (2021)
- [HARNet: A Convolutional Neural Network for Realized Volatility Forecasting](https://mlsanity.com/paper/2205.07719/) (2022)

## Built on by (most cited first, 6 of 6)

- [Rough Transformers: Lightweight and Continuous Time Series Modelling through Signature Patching](https://mlsanity.com/paper/2405.20799/) (2024)
- [RegimeFolio: A Regime Aware ML System for Sectoral Portfolio Optimization in Dynamic Markets](https://mlsanity.com/paper/2510.14986/) (2025)
- [Partially Stochastic Infinitely Deep Bayesian Neural Networks](https://mlsanity.com/paper/2402.03495/) (2024)
- [Rough Transformers for Continuous and Efficient Time-Series Modelling](https://mlsanity.com/paper/2403.10288/) (2024)
- [ReLaMix: Residual Latency-Aware Mixing for Delay-Robust Financial Time-Series Forecasting](https://mlsanity.com/paper/2603.20869/) (2026)
- [TimeTok: Granularity-Controllable Time-Series Generation via Hierarchical Tokenization](https://mlsanity.com/paper/2605.01418/) (2026)

## Abstract (arXiv)

Volatility forecasts play a central role among equity risk measures. Besides traditional statistical models, modern forecasting techniques based on machine learning can be employed when treating volatility as a univariate, daily time-series. Moreover, econometric studies have shown that increasing the number of daily observations with high-frequency intraday data helps to improve volatility predictions. In this work, we propose DeepVol, a model based on Dilated Causal Convolutions that uses high-frequency data to forecast day-ahead volatility. Our empirical findings demonstrate that dilated convolutional filters are highly effective at extracting relevant information from intraday financial time-series, proving that this architecture can effectively leverage predictive information present in high-frequency data that would otherwise be lost if realised measures were precomputed. Simultaneously, dilated convolutional filters trained with intraday high-frequency data help us avoid the limitations of models that use daily data, such as model misspecification or manually designed handcrafted features, whose devise involves optimising the trade-off between accuracy and computational efficiency and makes models prone to lack of adaptation into changing circumstances. In our analysis, we use two years of intraday data from NASDAQ-100 to evaluate the performance of DeepVol. Our empirical results suggest that the proposed deep learning-based approach effectively learns global features from high-frequency data, resulting in more accurate predictions compared to traditional methodologies and producing more accurate risk measures.