Mapping the Trend, Application and Forecasting Performance of Asymmetric GARCH Models: A Review Based on Bibliometric Analysis
Bibliographic record
Abstract
The past few years have witnessed renewed interest in modelling and forecasting asymmetry in financial time series using a variety of approaches. The most intriguing of these strategies is the “asymmetric” or “leverage” volatility model. This study aims to conduct a review of asymmetric GARCH models using bibliometric analysis to identify their key intellectual foundations and evolution, and offers thematic and methodological recommendations for future research to advance the domain. Bibliometric analysis was used to identify patterns in and perform descriptive analysis of articles, including citation, co-authorship, bibliographic coupling, and co-occurrence analysis. The study located 856 research papers from the Scopus database between 1992 and 2021 using key phrase and reference search methods. Publication trends, most influential authors, leading countries, and top journals are described, along with a systematic review of highly cited articles. The study summarises the development, application, and performance evaluation of asymmetric GARCH models, which will help researchers and academicians significantly contribute to this literature by addressing gaps.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.040 | 0.058 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".