Financial Contagion: An Empirical Investigation of the Relationship BetweenFinancial-stress Indexes of Australia and the US
Bibliographic record
Abstract
A key departure in this study from many earlier studies is that, on the continuum of financial stress from nil to very high, both very high levels of stress and very low levels are seen as being harmful and potential harbinger of a financial-market crisis. Specifically, a surfeit of stress can act as a tipping point into crisis and a dearth of stress can encourage hubris and increase a nation’s susceptibility to financial contagion from another nation; even one that is far removed by geographic and/or economic distance. This paper focuses on developing financial stress indices for the US and Australia using composite market indices, trade weight indices and yields on securities with different maturity dates. Monthly data from January 1989 to December 2011 was sourced from the Australian Bureau of Statistics (ABS), the Reserve Bank of Australia (RBA), the Federal Reserve Bank (FRB), the Bureau of Economic Analysis (BEA), the Federal Reserve Bank of St Louis website, Bank of Canada, Reserve Bank of New Zealand and Yahoo finance website. For purposes of this study the aggregate measures of stress consists of inverted yield spreads, volatility measures for market indices, volatility measures of trade weighted indexes, risk spreads, credit risk spreads and a measures of risk in the equity market.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".