The Relationships between Foreign Exchange Volatility Skew and Jump Risk
Bibliographic record
Abstract
In this novel study, I investigate whether option implied volatility and implied volatility skew contain information capable of elucidating, in an ex-ante manner, the probability of exceptional foreign exchange price fluctuations. I study four of the most widely traded currency pairs and their corresponding options over varying option maturities and distinct definitions of volatility skew and price jumps, each over the period 1 Jan 2007 to 18 November 2013. I find significant evidence of such informational content in at-the-money implied volatility, implied volatility skew and currency forward premium, each with differing degrees of influence. Further, as opposed to results in existing literature on price jumps within the equity asset class, the ability of volatility skew to predict price jumps does not attenuate with an increasing option maturity up to three months. I also observe through probit marginal analysis how at-the-money implied volatility dominates in its influence of price jump probability, while currency carry and implied volatility skew hold smaller but nonetheless significant influence.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".