The relationship between a Unified Financial Condition Index and the most actively traded USD based Foreign Currency pairs
Bibliographic record
Abstract
Various regulatory bodies in the US use proprietary financial conditions indices as tools to measure the health of financial markets. While they share some common variables, they also differ in areas such as data frequencies of their respective models. The aim of this study is to propose a unified financial condition index centered around the most popular financial conditions indices used in the US and tests its relationship with the most actively traded USD based foreign currency pairs. Using weekly data over 1993-2018, this paper proposes a unified financial condition index (UFCI) under a principal component analysis framework. The index captures 78% of the variability inherent in St Louis Federal Reserve Financial Stress Index, the Chicago Fed National Financial Condition Index and the Adjusted National Financial Condition Index. Significant p-value of UFCI, homoscedasticity and a relatively stable root mean squared errors was observed only for EUR/USD. Mixed findings found as lags were increased suggests a weak relationship between UFCI and foreign currencies. The UFCI forecasting model is compared with the VIX (volatility index) based model, and also a random walk model. Although the UFCI model was superior only for the Canadian dollar, Chinese yuan and Indian Rupee after considering heteroscedasticity in errors, results were sensitive to number of lags and insignificant p-values.
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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 | 0.001 | 0.000 |
| 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 | 0.001 | 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".