  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.
