HARNet: A Convolutional Neural Network for Realized Volatility Forecasting
HARNet：用于已实现波动率预测的卷积神经网络AI 解读 ↓
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HARNet：用于已实现波动率预测的卷积神经网络
- 研究问题
- 如何将成熟的异质自回归波动率模型与深度神经网络衔接，使网络优化更稳定，并检验其能否改善已实现波动率预测？
- 方法与思路
- 作者构造分层膨胀卷积网络，使初始化预测与对应 HAR 基线完全一致，并研究以 QLIKE 为损失时的优化及预测表现。
- 材料陈述的贡献
- 摘要称初始化方案稳定了优化，三个股票指数上的评估显示预测准确性改善，并通过滤波权重分析描述历史信息的作用。
- 阅读关注点
- 核对基线复现与初始化构造，检查样本外预测、调参和损失比较，并辨明滤波权重能否充分说明历史信息的重要性。
- 阅读边界
- 这是 arXiv 预印本，同行评审或期刊接收未核实；摘要未给样本时段、改善幅度或显著性细节，滤波权重解释也不等于因果。
原文摘要
Despite the impressive success of deep neural networks in many application areas, neural network models have so far not been widely adopted in the context of volatility forecasting. In this work, we aim to bridge the conceptual gap between established time series approaches, such as the Heterogeneous Autoregressive (HAR) model, and state-of-the-art deep neural network models. The newly introduced HARNet is based on a hierarchy of dilated convolutional layers, which facilitates an exponential growth of the receptive field of the model in the number of model parameters. HARNets allow for an explicit initialization scheme such that before optimization, a HARNet yields identical predictions as the respective baseline HAR model. Particularly when considering the QLIKE error as a loss function, we find that this approach significantly stabilizes the optimization of HARNets. We evaluate the performance of HARNets with respect to three different stock market indexes. Based on this evaluation, we formulate clear guidelines for the optimization of HARNets and show that HARNets can substantially improve upon the forecasting accuracy of their respective HAR baseline models. In a qualitative analysis of the filter weights learnt by a HARNet, we report clear patterns regarding the predictive power of past information. Among information from the previous week, yesterday and the day before, yesterday's volatility makes by far the most contribution to today's realized volatility forecast. Moroever, within the previous month, the importance of single weeks diminishes almost linearly when moving further into the past.