Detecting Changes in Asset Co-Movement Using the Autoencoder Reconstruction Ratio

• URL: http://arxiv.org/abs/2002.02008v2
• Date: Sun, 27 Sep 2020 14:14:44 GMT
• Title: Detecting Changes in Asset Co-Movement Using the Autoencoder Reconstruction Ratio
• Authors: Bryan Lim, Stefan Zohren, Stephen Roberts
• Abstract summary: We propose a real-time indicator to detect temporary increases in asset co-movements. The Autoencoder Reconstruction Ratio measures how well a basket of asset returns can be modelled using a lower-dimensional set of latent variables.
• Score: 5.5616364225463055
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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