Multiple Horizon Causality in Network Analysis : Measuring Volatility Interconnections in Financial Markets

Existing literature cannot provide economic and financial networks with a unified measure to estimate network spillovers for empirical studies. In this paper, we propose a novel time series econometric method to measure high-dimensional directed and weighted market network structures. Direct and spillover effects at different horizons, between nodes and between groups, are measured in a unified framework. We infer causality effects in the network through a causality measure based on flexible VAR models specified by the LASSO approach. (Non-sparse) network structures can be estimated from a sparse set of model parameters. To summarize complex estimated network structures, we also proposed three connectedness measures that fully exploit the flexibility of our network measurement method. We apply our approach to investigate the implied volatility interconnections among the S&P 100 stocks over the period of 2000-2015 as well as its subperiods. We find that 7 out of the 10 most influential firms in the S&P 100 belong to the financial sector. Top investment banks (Morgan Stanley, Goldman Sachs and Bank of America) have the greatest influence in the financial sector. Market connectedness is especially strong during the recent global financial crisis, and this is mainly due to the high connectedness within the financial sector and the spillovers from the financial sector to other sectors