Dependency Structure Analysis of the Japanese Stock Market Based on Realized Networks

Abstract

The covariance of the log-return of financial assets is a fundamental element in a wide range from asset allocation to risk management. In this paper, we execute an empirical analysis using a high-dimensional realized covariance estimation method proposed by Brownlees et al. (2018) which performs lasso regularization. Specifically, we first estimate the sparse inverse integrated covariance matrix of some assets listed on Nikkei stock average in 2016 and create networks of the market. Then we analyze it from the network perspective and compare the network structure of each industrial sector. As a result, we conclude that the Japanese stock market does not depend on one company, that is, it is not a scale-free network. Furthermore, we confirm that the network structure moves to correspond to the Nikkei stock average.