MétaCan
Menu
Back to cohort
Record W4388344700 · doi:10.3905/jod.2023.1.195

VIX Option Pricing for Non-Parameter Heston Stochastic Local Volatility Model

2023· article· en· W4388344700 on OpenAlex
Junmei Ma, Wei Xu

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Journal of Derivatives · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicStochastic processes and financial applications
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsLocal volatilityStochastic volatilityHeston modelValuation of optionsMonte Carlo methodEconometricsExotic optionParametric statisticsComputer scienceMathematicsApplied mathematicsMathematical optimizationVolatility (finance)SABR volatility modelStatistics

Abstract

fetched live from OpenAlex

The Heston-Dupire model is a well-established stochastic local volatility model that offers a non-parametric representation. This model is known to closely match the implied volatility surface of options observed in the market. However, due to its non-parametric local component, Monte Carlo simulation is the only viable numerical method for derivative pricing under this model. This article proposes a novel willow tree method to replace Monte Carlo simulation for pricing exotic options and VIX options under the Heston-Dupire model. We provide the convergence rate of this method and conduct several numerical experiments to demonstrate its accuracy and efficiency. Our proposed method offers an alternative numerical technique that can enhance the computational efficiency of pricing derivatives under the Heston-Dupire model.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.804
Threshold uncertainty score0.304

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.051
GPT teacher head0.270
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it