Analysing currency options market downturn during global financial crisis (2007-2010)
Bibliographic record
Abstract
The 24 hours of foreign exchange (FX) market was extremely volatile during the global financial crisis (GFC) due to increased uncertainty about global financial assets' market value and the impact of financial stress on economic performance. The derivative markets had also been partially blamed for the GFC. Further, the Bank for International Settlement (BIS) survey reported a significant downturn in the currency options average daily turnover in the GFC period. Therefore, the objective of this thesis is to analyse the currency options market downturn during GFC. We employed Australian dollar (AUD), Canadian dollar (CAD), Swiss franc (CHF), Euro (EUR) and British pound (GBP) options with one-month, two-month and three-month maturity to analyse three currency options market situations. First, the currency options market efficiency was examined and discussed the influence of currency options mispricing in dropping the average daily currency options turnover. Second, the currency options implied volatility smirk was estimated due to mispricing of out-of-the-money (OTM) put options. Third, underlying currencies of options were assessed as a safe-haven currency. We found AUD, CAD, CHF, and GBP options were mispriced, costly for trading and therefore, traders were reluctant to use these currency options during GFC. Further, the highly volatile AUD, CAD, CHF, and GBP market increased the demand for OTM put options to the risk-averse FX investors that let options be mispriced. Interestingly, the downturn in the currency options market growth during GFC is the combined effects of the currency options market efficiency; the currency options market implied volatility smirk and currency safe-haven attributes. It implies that traders should consider currency options market efficiency, smirk, and safe-haven issues to trade currency options effectively without undesirable costs. Our results also revealed the EUR combination of several currencies worked as a portfolio that protected its value against the US dollar during GFC.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 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".