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.

翻译：波动率预测在股权风险度量中占据核心地位。除了传统的统计模型外，将波动率视为单变量日度时间序列时，亦可采用基于机器学习的现代预测技术。此外，计量经济学研究表明，通过高频日内数据增加每日观测值有助于改进波动率预测。本文提出DeepVol模型，该模型基于扩张因果卷积，利用高频数据预测次日波动率。我们的实证结果表明，扩张卷积滤波器能高效地从日内金融时间序列中提取相关信息，证明该架构可有效利用高频数据中的预测信息——若预先计算已实现测度，这些信息将丢失。同时，使用日内高频数据训练的扩张卷积滤波器帮助我们规避了仅使用日度数据模型的局限性，例如模型设定错误或手动设计的特征工程，这些设计需在精度与计算效率间权衡优化，且易导致模型难以适应环境变化。本分析采用纳斯达克100指数两年的日内数据评估DeepVol性能。实证结果表明，所提出的深度学习方法能有效从高频数据中学习全局特征，相比传统方法产生更准确的预测，并提供更精确的风险度量。