European option pricing for GARCH dynamic infinite activity Lévy processes based on parameter learning
Bibliographic record
Abstract
In this paper,we consider a three-dimension state space model for establishing a discrete-time dynamic Levy process,including time-varying drift,conditional volatility and stochastic jump activity.Then we obtain the equivalent non-arbitrage pricing model through local risk-neutral valuation relationship(RNVR).Taking non-Gaussian ARMA-NGARCH model as our benchmark,we construct a discrete time dynamic Levy process with GARCH effect for modeling SP500 index.Furthermore we jointly estimate the parameters of the model and study the option pricing performance based on Bayesian learning approach.Research results show that our dynamic Levy process can depict the time-varying drift rate,conditional volatility and infinite activity styles.Meanwhile,Bayesian approach improves the option valuation ability of our model.Infinite jump models are significant superior and increase the pricing accuracy of implied volatility.We also find that unscented particle filtering(UPF) has the best estimation performance,nonGaussian models in the yield prediction are of no significant difference,but the rapidly decreasing tempered stable processes(RDTS) have minimum errors for option pricing.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".