A New Look at Copper Markets: A Regime-Switching Jump Model
Bibliographic record
Abstract
GARCH-jump models of metal price returns, while allowing for sudden movements (jumps), apply the same specification of the jump component in both 'bear'and 'bull' markets. As a result, the more frequent but relatively small jumps that occur in both bear and bull markets dominate the characterization of the jump process. Given that large jumps, although less frequent, are still quite common in copper (and other metal) markets, this is a potential shortcoming of current models. More flexibility can be added to the modeling process by allowing for regime-switching. In this paper we specify a model that allows for switching across two separate regimes, with the possibility of different jump sizes and frequencies under each regime, along with a regime-specific GARCH process for the conditional variance. This model is applied to daily copper futures prices over the period of January 2 1980 through the end of July 2007. The model is estimated both with and without factors such as interest and exchange rate movements entering into the specification of the state-dependent mean of the conditional jump size. In some respects, a Regime Switching GARCH-Jump Model performs well when applied to the copper returns data. The results are mixed in terms of whether or not variations of the model that allow jump sizes to be a function of interest or exchange rates offer much of an advantage over a pure time series approach to the modeling of copper returns over the past three decades.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".