Realized Volatility and Stylized Facts of Chinese Treasury Bond Market
Bibliographic record
Abstract
Based on high frequency data, this paper studies the volatility stylized facts of Chinese Treasury bond market (CTBM) in detail, including the best sampling frequency selected to compute the realized volatility, the conditional and unconditional distribution of the returns, the long memory property, the intraday, inter-day pattern of the returns and volatility, the asymmetry of volatility, and so on. The main conclusions about CTBM volatility are provided. 15 minute is best sampling frequency. The RV-based conditional distribution of return is nearly normal. Both return and volatility have significant inter-day but insignificant intraday periodicity. Moreover, the volatility asymmetry existing widely in stock or exchange market is not significant in Chinese Treasury bond market. Key words: Realized volatility, Chinese Treasury bond market, High frequency data Resume: Base sur des donnees de haute frequence, le present article etudie en detail la volatilite des faits stylises du Marche de bon du Tresor chinois (MBTC), comprenant la meilleure frequence de prelevement selectionnee pour calculer la volatilite realisee, la distribution conditionnelle et inconditionnelle des retours, la propriete de longue memoire, le modele intrajour et interjour des retours et la volatilite, l’asymetrie de volatilite, etc. Les conclusions principales sur la volatilite du MBTC sont les suivantes : 15 minutes est la meilleure frequence de prelevement, la distribution conditionnelle RV-base du retour est presque normale. Le retour et l’asymetrie de volatilite ont tous les deux une periodicite inter-jour signifiante, mais une periodicite intrajour insignifiante. D’ailleurs, l’asymetrie de volatilite existant amplement dans la bourse et le marche des changes n’est pas importante sur le Marche de bon du Tresor chinois. Mots-Cles: volatilite realisee, Marche de bon du Tresor chinois, donnees de haute frequence
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".