Fuzzy Option Pricing with Data-Driven Volatility using Novel Monte-Carlo Approach
Bibliographic record
Abstract
Fuzzy numbers play an important part in the advanced theory of computational finance, which is undergoing a revolution aided by the powerful simulation and data-driven models. The volatility parameter is the most important parameter for pricing short-term options and arguably one of the important parameters for all financial time series. Traditional option pricing models determine the option's expected return without taking into account the uncertainty associated with the underlying asset price volatility. Fuzzy set theory can be used to explicitly account for such uncertainty. There has been a growing interest in studying fuzzy option pricing for European options and binary options using hybrid models. However, those fuzzy coefficient hybrid models are not data-driven. This paper uses data-driven volatility forecasts to study fuzzy European option pricing. A simple yet effective fuzzy option pricing model incorporating data-driven exponentially weighted moving average (EWMA) and neuro volatility models is presented to obtain fuzzy volatility forecasts and fuzzy option prices. The dynamics of the underlying assets is complex and hence it is preferable to use the Monte Carlo (MC) simulation, to get rid of the assumptions in the commonly used Black Scholes (BS) models. Using the data-driven fuzzy a-cuts of the annualized volatility, a-cuts of the call and put option prices based on novel MC simulation are obtained using$t$or Laplace distributed asset log returns. The driving idea, unlike the existing MC option pricing with normality assumption, is that the proposed novel MC method uses the data-driven$t$or Laplace distribution of asset log returns and nonlinear adaptive fuzzy volatility.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".