Bibliographic record
Abstract
The traditional pricing methods of warrants assume that the yield of the underlying stock follows the log-normal probability distribution.But in reality it failed to take into account the experiential phenomenon of the financial time series,such as the cluster and fat tailor phenomena of the distribution;the aggregation of volatility;the leverage effect in securities market,etc,which may lead to price variance between theory and practice.In this paper,historical volatility is replaced by stochastic volatility to remove the impact of conditional heteroscedasticity of financial time series.GARCH Models(including GARCH,EGARCH,GJR-GARCH) will be used to estimate the parameters of the yield of the underlying stock and to price the warrants.Further more,we will analyze and discuss the differences between historical volatility and stochastic volatility;between symmetric and asymmetric GARCH models;and between theory and practice.In the conclusion,besides the imper-fect theoretical model and the incomplete trade systems,the speculativeness of China Stock Markets is the main reason of such price variance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".