From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks
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利用半球神经网络实现主动与反应式波动建模
- 研究问题
- 在宏观经济密度预测中，如何使高参数化神经网络同时学习条件均值和方差，控制过拟合，并结合先行指标与既往预测误差预测波动？
- 方法与思路
- 作者设计共享入口核心的均值与方差双分支网络，通过波动强调约束处理两者不确定性，并使用分块袋外检验控制过拟合，以最大似然训练。
- 材料陈述的贡献
- 摘要称，HNN 可结合主动与反应式波动预测，样本外实验中的均值、方差及概率预测表现良好，并可与结构化深度学习模型结合。
- 阅读关注点
- 应核对均值与方差分支的约束、分块袋外检验的实施及信息时点，检查样本外划分、基准模型调参与密度预测评价指标的可比性。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未经核实；摘要未给出预测目标、样本区间与误差数值，所述良好表现不能推广到未经测试的场景。
原文摘要
We reinvigorate maximum likelihood estimation (MLE) for macroeconomic density forecasting through a novel neural network architecture with dedicated mean and variance hemispheres. Our architecture features several key ingredients making MLE work in this context. First, the hemispheres share a common core at the entrance of the network which accommodates for various forms of time variation in the error variance. Second, we introduce a volatility emphasis constraint that breaks mean/variance indeterminacy in this class of overparametrized nonlinear models. Third, we conduct a blocked out-of-bag reality check to curb overfitting in both conditional moments. Fourth, the algorithm utilizes standard deep learning software and thus handles large data sets - both computationally and statistically. Ergo, our Hemisphere Neural Network (HNN) provides proactive volatility forecasts based on leading indicators when it can, and reactive volatility based on the magnitude of previous prediction errors when it must. We evaluate point and density forecasts with an extensive out-of-sample experiment and benchmark against a suite of models ranging from classics to more modern machine learning-based offerings. In all cases, HNN fares well by consistently providing accurate mean/variance forecasts for all targets and horizons. Studying the resulting volatility paths reveals its versatility, while probabilistic forecasting evaluation metrics showcase its enviable reliability. Finally, we also demonstrate how this machinery can be merged with other structured deep learning models by revisiting Goulet Coulombe (2022)'s Neural Phillips Curve.