Realized Volatility Analysis from Various Perspectives Based on Hilbert Huang Transform
Bibliographic record
Abstract
In this paper, based on results of the volatility of stock returns after the Hilbert Huang Transform, to research the influential factors of volatility composition, the influential factor model of yield volatility is established. This model studies the volatility from three angles respectively: the hysteresis of impact, the influence degree and the affect correlation. For the hysteresis of impact, this paper uses the model to determine lag phases of different IMF of volatility. For the influence degree, after using principal component analysis to eliminate the multicollinearity between different IMF, we calculate direct contribution, correlation coefficient and variable coefficient to quantify the influence degree of IMF on RV, BV and JV, the independence degree and the information abundance. For affect correlation, this paper adopts four different distance calculating methods and grey correlation method to depict the connection degree between RV and IMFin different dimensions. Finally, this paper uses the data of China's financial markets to carry on the empirical analysis, and explores various characteristics of realized volatility through comprehensive influence degree, in order to provide new perspectives and ideas for financial analysis and forecast. provide new perspectives and ideas for financial analysis and forecast.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".