论文标题
使用自动编码器重建比检测资产共同移动的变化
Detecting Changes in Asset Co-Movement Using the Autoencoder Reconstruction Ratio
论文作者
论文摘要
检测资产共同发展的变化对金融从业人员而言至关重要，由于及时发现历史相关性的崩溃，带来了许多风险管理的好处。在本文中，我们提出了一个实时指标，以检测资产共同发展的临时增加，即自动编码器重建比，该比率衡量了可以使用较低维度的潜在变量集对资产返回篮子的建模。 ARR使用深度稀疏的DeNoising自动编码器对返回矢量进行维度降低，从而取代了标准吸收比的PCA方法，并为非高斯回报提供了更好的模型。通过在CRSP美国总市场指数上进行预测的系统性风险应用，我们表明，较低的ARR值与较高的波动性和较大的下降相吻合，这表明资产增加的共同发展确实与市场疲软的时期相对应。我们还证明，可以通过包括其他ARR输入来改善实现波动性和市场崩溃的短期（即5分钟和1小时）的预测指标。
Detecting changes in asset co-movements is of much importance to financial practitioners, with numerous risk management benefits arising from the timely detection of breakdowns in historical correlations. In this article, we propose a real-time indicator to detect temporary increases in asset co-movements, the Autoencoder Reconstruction Ratio, which measures how well a basket of asset returns can be modelled using a lower-dimensional set of latent variables. The ARR uses a deep sparse denoising autoencoder to perform the dimensionality reduction on the returns vector, which replaces the PCA approach of the standard Absorption Ratio, and provides a better model for non-Gaussian returns. Through a systemic risk application on forecasting on the CRSP US Total Market Index, we show that lower ARR values coincide with higher volatility and larger drawdowns, indicating that increased asset co-movement does correspond with periods of market weakness. We also demonstrate that short-term (i.e. 5-min and 1-hour) predictors for realised volatility and market crashes can be improved by including additional ARR inputs.
