WAVELET TEST OF MULTIFRACTALITY OF ASIA-PACIFIC INDEX PRICE SERIES
Bibliographic record
Abstract
This paper argues for the superiority of multifractal over ARCH methods where the objective is to understand market microstructure based on accurate volatility modeling. The paper examines the multifractality of index price series on daily data of Nikkei 225, All Ordinaries, Hang Seng, KLSE Composite and Straits Times Index. Wavelets, short form waves with local support are used for time/scale decomposition of financial time series. The multifractal spectrum (MFS) of daily index prices is calculated with Wavelet Transform Modulus Maxima method described in Yalamova (2003). The MFS may reveal trading time irregularities suggested by the Multifractal Model of Asset Returns (Calvet & Fisher, 2002). The trading time deformation process may uncover information on the efficiency of the trading system that would be useful for regulatory and reorganization purposes. Multifractals describe the cascade of volatility of returns and are suited for research at different time scales simultaneously unlike ARCH type models. In addition, this method provides dimension estimates for the detection of emerging chaotic patterns. The Hurst exponent calculated from the scaling function indicates persistence in volatility of index returns. The choice of data around the October 1997 drawdown is based on the scientific evidence that markets as complex dynamical systems reveal their properties better in extreme conditions.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".