A new multilayer network construction via Tensor learning
This provides a tool for risk managers to understand dependencies across market, liquidity, and volatility risks, but it is incremental as it builds on existing tensor methods for a specific domain.
The authors tackled the problem of constructing multilayer networks from data by proposing a Tucker tensor autoregression method, which captures within- and between-layer connections and applies filtering for visualization, showing strong interconnections between volumes and prices layers and fewer between uncertainty measures in financial risk data.
Multilayer networks proved to be suitable in extracting and providing dependency information of different complex systems. The construction of these networks is difficult and is mostly done with a static approach, neglecting time delayed interdependences. Tensors are objects that naturally represent multilayer networks and in this paper, we propose a new methodology based on Tucker tensor autoregression in order to build a multilayer network directly from data. This methodology captures within and between connections across layers and makes use of a filtering procedure to extract relevant information and improve visualization. We show the application of this methodology to different stationary fractionally differenced financial data. We argue that our result is useful to understand the dependencies across three different aspects of financial risk, namely market risk, liquidity risk, and volatility risk. Indeed, we show how the resulting visualization is a useful tool for risk managers depicting dependency asymmetries between different risk factors and accounting for delayed cross dependencies. The constructed multilayer network shows a strong interconnection between the volumes and prices layers across all the stocks considered while a lower number of interconnections between the uncertainty measures is identified.