DeepVol：基于膨胀因果卷积的高频数据波动率预测
DeepVol: Volatility Forecasting from High-Frequency Data with Dilated Causal Convolutions
AI总结：
本文提出 DeepVol 模型，利用膨胀因果卷积从高频日内数据中自动学习预测信息以预测次日波动率，在 NASDAQ-100 两年数据上验证其优于传统方法，并避免了日度数据模型的设定错误和人工特征设计局限。
摘要：
波动率预测在股票风险度量中占据核心地位。除传统统计模型外，当把波动率视为单变量日度时间序列时，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，利用高频日内数据增加每日观测数量有助于改善波动率预测。本文提出 DeepVol，一种基于膨胀因果卷积的模型，利用高频数据预测次日波动率。实证结果表明，膨胀卷积滤波器在从日内金融时间序列中提取相关信息方面非常有效，证明该架构能够有效利用高频数据中存在的预测信息，而这些信息若预先计算已实现测度将会丢失。同时，使用日内高频数据训练的膨胀卷积滤波器有助于规避使用日度数据模型的局限性，例如模型设定错误或人工设计特征，这些特征的设计涉及在准确性与计算效率之间进行权衡优化，使模型容易缺乏对变化环境的适应性。在分析中，我们使用 NASDAQ-100 两年的日内数据评估 DeepVol 的性能。实证结果表明，所提出的基于深度学习的方法能够有效从高频数据中学习全局特征，与传统方法相比能产生更准确的预测，并生成更准确的风险度量。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
