Bibliographic record
Abstract
The first paper examines the properties of the realized volatilities of US Dollar / Canadian Dollar spot exchange rate covering a time span of about three years and then the deseasonalized volatilities are estimated and forecasted using a fractionally-integrated model. The key feature of the realized volatilities is that they are model-free and also approximately measurement-error-free. Usually a U-shaped pattern of the intraday volatilities should be observed due to opening-closure effects in the global market. I do not see a typical U-shaped pattern in the intraday volatilities for US Dollar / Canadian Dollar. The reasons are given in this paper. I use ARFIMAX model to estimate and forecast the deseasonalized volatilities and the results are promising. The second paper proposes a time series based trading strategy for 'pairs trading'. Pairs trading is one of the oldest statistical arbitrage strategies and has been proved to be successful on Wall Street. Most academic studies on pairs trading focus on pair selection or optimal threshold comparison. This is the first paper to introduce time series methodology into research of pairs trading. The dynamics of the spread between two stocks in a pair are tested and examined. A time series 'dynamic threshold method' is proposed in this paper and the trading strategy based on this method improves the excess return of traditional naïve pairs trading model significantly.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".