3 Methodology
3.1 Scenario analysis
Scenario analysis is a supplementary tool used to examine risks, such as liquidity risk, that are difficult to model through statistical analysis. Liquidity risk is difficult to model because financial market participants do not respond mechanically to changing fundamentals and may, for short periods, be subject to herding behaviour based on uncertainty and other psychological factors. Markets might lend one day based on a belief that the growing stock of debt can be reversed, then not at all the next.
Risks, such as liquidity risk, occur infrequently or may not show up in statistical records at all. Infrequent risks - referred to as tail risks[3] - are particularly challenging for governments as they are expected to remain operational. While traditional risk models rely on statistical records to model impact and probability, scenario analysis makes no adjustment for the expected probability. A direct examination of the size of a shock and its impacts allows examination of the Crown's ability to handle large shocks. In this way, scenario analysis provides additional information to results provided by other work on Crown risk (refer Irwin and Parkyn, 2009).
Notes
- [3]Tail in this case refers to events in the tail of a probability distribution. Statistics may underweight events which occur infrequently or may even be absent from samples. However, modern risk management techniques are rapidly evolving. Nicolas Talib (2007) suggests that statistically unprecedented events should not be ignored. Collectively these tail events, while rare, may, in aggregate, have the potential to radically alter the course of subsequent policy. For example, the global crisis was almost without statistical precedent, but has had a greater impact on fiscal policy than the smaller and more frequent annual fluctuations associated with the business cycle.
