Examining realized volatility regimes under a threshold stochastic volatility model
Bibliographic record
Abstract
ABSTRACT This paper examines the realized volatility regimes under a threshold stochastic volatility (SV) model framework. Due to the availability of the volatility proxy, the estimation of the models' parameters can be easily implemented via standard maximum likelihood estimation (MLE) rather than using simulated Bayesian methods. In addition, the proposed model accommodates state‐dependent correlations between the return and volatility processes. This new feature can not only explain the so‐called leverage effect under the threshold SV framework, but also increase the flexibility of the model structure. Several mis‐specification and sensitivity experiments are conducted using Monte Carlo methods. In the empirical study, we apply the threshold SV structure to three stock indices. The results show that in different regimes, the returns and volatilities exhibit asymmetric behavior. In addition, this paper allows the threshold in the model to be flexible (or data driven) and uses a sequential optimization based on MLE to search for the ‘optimal'threshold value. We find that the model with a flexible threshold is always preferred to the traditional model with a fixed threshold according to the standard log‐likelihood measure. Interestingly, the ‘optimal’ threshold is found to be stable across different sampling realized volatility measures. Copyright © 2012 John Wiley & Sons, Ltd.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".