A Network Approach to Volatility Diffusion and Forecasting in Global Financial Markets

The node degree distribution of an inferred financial network is often characterized by a small number of nodes with a large number of connections and many nodes with few connections. To date, there is no empirical evidence on how this stylized statistical fact can be useful in predicting fluctuations of financial assets. In this paper, we explore this possibility by modifying well-known time-series models and augmenting them with covariates from a reconstructed network, selecting nodes that are identified as the most connected to the index of interest. We then analyze the out-of-sample performance of these models across different volatility proxies. The results show that nodes belonging to the right tail of the degree distribution possess high predictive power over financial aggregates, independently of the volatility measure used. Our findings suggest that incorporating the topological information that arises from this statistical regularity in financial networks can enhance the accuracy of traditional predictive models.