The US Financial Crisis and the Behavior of the Foreign Exchange Market
Bibliographic record
Abstract
Foreign exchange market is the most active market in today’s global financial domains. While the consensus on several aspects of this market is fairly established, the informational efficiency in this market is still unsettled, particularly during unexpected interruptions and unusual or unstable periods. The financial crisis of 2008 is the most recent example of such a period. This dissertation focuses on the efficiency of the foreign exchange market during a unique, turbulent period using the six most actively traded currencies: the Australian dollar, Canadian dollar, Swiss franc, Euro, British pound, and Japanese yen. Considering nine months before the peak of the financial crisis to nine months thereafter, the entire sample is divided into three sub-samples: full-, non-crisis-, and crisis-periods. Both daily and minute-by-minute data are used. A variety of instruments are analyzed, including spot, forward, and exchange traded funds on the currencies. The methodologies that are employed range from standard econometric tests of efficiency to estimation of vector error correction models to identify price discovery, or leadership positions, in each of the currency markets. The findings indicate behavioral similarities and differences. The patterns of the volatility of the currencies are mixed: two-humped for the AUD, CAD, and EUR; W-shaped for the CHF; three-humped for the GBP, and flat U-shaped for the JPY. The daily results from several methodologies provide mixed evidence on market efficiency. Over the entire sample period, the estimated forward premium coefficients from the GARCH (1, 1) model are not significant for all currencies, while the null hypotheses of zero and one cointegrating vectors cannot be rejected for all currencies, except for the AUD. These findings are consistent with some of the previous studies, concluding that the efficiency tests in the foreign exchange market would depend on the methodology and the time period of the study. The high frequency data results show different degrees of price discovery between pair-wise instruments. Specifically, the spot exchange market shows a greater contribution to price discovery than the corresponding exchange traded funds. A possible explanation is the current size of the market and its increased transparency through the use of electronic trading.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".