---
title: Calibration of Local Volatility Models under the Implied Volatility Criterion
url: https://www.ml-quant.com/papers/ssrn/4801520/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4801520
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4801520
featured: 2024-04-24
citations: 0
topic: Derivatives & Volatility
---


# Calibration of Local Volatility Models under the Implied Volatility Criterion

A study introduces a new calibration criterion for local volatility models that minimizes the gap between theoretical and market implied volatilities, balancing calibration error reduction and overfitting prevention.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4801520
- Identifier: SSRN 4801520
- Released: 2024-04-20
- First featured: Quant Letter No. 46 (2024-04-24): https://www.ml-quant.com/issues/2024-04-24/
- Citations (Semantic Scholar): 0
- Published in: not yet
- Topic: Derivatives & Volatility

## Related

- [Decentralized and Centralized Options Trading: A Risk Premia Perspective](https://www.ml-quant.com/papers/ssrn/4822783/): The research looks at OnChain options traded on a decentralized Ethereum blockchain exchange, underlining the differences in implied volatilities compared to OffChain options traded on centralized exchanges.
- [Convex Volatility Interpolation](https://www.ml-quant.com/papers/ssrn/4831218/): Convex Volatility Interpolation (CVI), a new method for calibrating implied volatility surfaces using quadratic programming, has been introduced, eliminating the need for hyperparameter tuning.
- [Operator Deep Smoothing for Implied Volatility](https://www.ml-quant.com/papers/arxiv/2406.11520/): A novel method for smoothing implied volatility using neural operators is presented, which maps data to smoothed surfaces, respects no-arbitrage rules, and is robust to input subsampling.
- [Subleading Correction to the Asian Options Volatility in the Black-Scholes Model](https://www.ml-quant.com/papers/doi/10-1142-s021902492350005x/): The study improves the pricing accuracy of Asian options by deriving the subleading correction to the implied volatility in the Black-Scholes model, which is determined by the large deviations property for the time-average of the geometric Brownian motion.
- [Kullback-Leibler Barycentre of Stochastic Processes](https://www.ml-quant.com/papers/arxiv/2407.04860/): The article presents a method that merges expert models using diffusion processes and deep learning, specifically for combining implied volatility smiles models from various datasets.
- [No-Arbitrage Deep Calibration for Volatility Smile and Skewness](https://www.ml-quant.com/papers/arxiv/2310.16703/): The introduction of a Derivative-Constrained Neural Network (DCNN) enhances the calibration of implied volatility surface in option prices, aiding in understanding market dynamics and risk management.