ESTIMATING BIVARIATE GARCH-JUMP MODEL BASED ON HIGH FREQUENCY DATA: THE CASE OF REVALUATION OF THE CHINESE YUAN IN JULY 2005
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Bibliographic record
Abstract
This paper analyzes the behavior of one-minute high-frequency time-series data of exchange rates for five currencies (Japanese Yen, Australian Dollar, Canadian Dollar, Euro, and Pound Sterling) against the US Dollar when the Chinese Yuan was revalued on July 21st, 2005. The data show the following distinctive features: (1) There is a large jump in the exchange rates time series at the time of the Yuan revaluation. (2) Large volatility in the returns of exchange rates is observed for a while after the jump. (3) There are many other jumps, possibly correlated, in each exchange rate time series. To capture these features we fit the following models to the data: (i) a univariate GARCH-Jump model with a large jump that is influential on volatility, and (ii) a bivariate GARCH-Jump model with correlated Poisson jumps. For comparison, we also estimate these GARCH models without the associated jumps. The model performance is evaluated based on Value-at-Risk (VaR).
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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 | 0.013 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
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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 it