Multiscale and partial correlation networks analysis of risk connectedness in global equity markets

This paper investigates the multiscale properties and the evolution patterns of risk connectedness in global equity markets using the sample from January 03, 2000 to December 24, 2018. GARCH–EVT–VaR model is used to measure systemic risk of global equity markets. We deal with the application of Univariate Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model and Extreme Value Theory (EVT) to measure the Value at Risk (VaR) of global equity markets. We then introduce the maximal overlap discrete wavelet transform (MODWT) method and partial correlation coefficients into the complex network theory to construct multiscale and partial correlation networks in global equity markets. We find the evidence of the strong risk connectedness among global equity markets in both the overall and multiscale networks, and their topological properties vary across time–frequency horizons. The US and Eurozone equity markets play a predominant role in the process of risk transmission. Most developing markets seem to remain inactive in the multiscale networks of risk connectedness. The rolling-window analysis based on time–frequency domain demonstrates that risk networks tend to be more concentrated not only at the mid-term scales but also during the financial crisis. Empirical results reveal multiscale risk connectedness characteristics, providing useful information for regulators to formulate macro prudential supervision policy and investors to update the portfolio strategies and risk prevention measures.