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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.

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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 the home to the most consistently systemically risky firms in the economy. The mining sector becomes occasionally as systemically risky as the financial sector, reflecting the importance of understanding the interrelationships between the financial sector and the real economy in monitoring systemic risks.

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.

We study financial volatility during the global financial crisis and use the largest volatility shocks to identify major events during the crisis. Our analysis makes extensive use of high frequency (HF) financial data to model volatility and, importantly, to determine the timing within the day when the largest volatility shocks occurred. The latter helps us identify the events that may be associated with each of these shocks, and serves to illustrate the benefits of using high-frequency data. Some of the largest volatility shocks coincide, not surprisingly, with the bankruptcy of Lehman Brothers on September 15, 2008 and Congress's failure to pass the Emergency Economic Stabilization Act on September 29, 2008. The day with the largest volatility shock was February 27, 2007, the date when Freddie Mac announced a stricter policy for underwriting subprime loans and a date that was marked by a crash on the Chinese stock market. However, the intraday HF data shows that the main culprit was a computer glitch in the trading system. The days with the largest drops in volatility can in most cases be related to interventions by governments and central banks.

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