We provide empirical evidence on the degree of systemic risk in Australia before, during and after the global financial crisis. We calculate a daily index of systemic risk from 2004 to 2013 in order to understand how real economy firms influence the outcomes for the rest of the economy. This is done via a mapping of the interconnectedness of the financial and non‐financial sectors. The financial sector is in general home to the most consistently systemically risky firms in the economy. The materials sector occasionally becomes as systemically risky as the financial sector, reflecting the importance of understanding these linkages.

We propose a methodology to include night volatility estimates in the day volatility modeling problem with high-frequency data in a realized generalized autoregressive conditional heteroskedasticity (GARCH) framework, which takes advantage of the natural relationship between the realized measure and the conditional variance. This improves volatility modeling by adding, in a two-factor structure, information on latent processes that occur while markets are closed but captures the leverage effect and maintains a mathematical structure that facilitates volatility estimation. A class of bivariate models that includes intraday, day, and night volatility estimates is proposed and was empirically tested to confirm whether using night volatility information improves the day volatility estimation. The results indicate a forecasting improvement using bivariate models over those that do not include night volatility estimates.