---
title: SPX Implied Volatility Inconsistencies
url: https://www.ml-quant.com/papers/ssrn/4684016/
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 4684016
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4684016
featured: 2024-01-09
citations: unknown
topic: Derivatives & Volatility
---


# SPX Implied Volatility Inconsistencies

Research using SPX options data from 2011 to 2022 found that Volterra Bergomi models do not accurately capture implied volatility due to the roughness component's structural limitations.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4684016
- Identifier: SSRN 4684016
- Released: 2024-01-04
- First featured: Quant Letter No. 32 (2024-01-09): https://www.ml-quant.com/issues/2024-01-09/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Derivatives & Volatility

## Related

- [The implied volatility surface (also) is path-dependent](https://www.ml-quant.com/papers/arxiv/2312.15950/): A new forecasting model is suggested that uses past returns and their squares to predict implied volatility surfaces and underlying asset returns for up to two years.
- [Computing Volatility Surfaces using Generative Adversarial Networks with Minimal Arbitrage Violations](https://www.ml-quant.com/papers/arxiv/2304.13128/): The article introduces a generative adversarial network (GAN) method for calculating volatility surfaces, showing that the GAN model is more accurate and faster than artificial neural network (ANN) methods.
- [Smile Consistent Basket Skew](https://www.ml-quant.com/papers/ssrn/4702005/): A paper introduces analytical approximations for the skew and convexity of an option on a basket of assets, which can be used to estimate the basket implied volatility at strikes around the ATM point and sufficiently small volatility or maturity.
- [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.
- [Pricing VIX Derivatives in a Stochastic Volatility Model based on CBI Processes](https://www.ml-quant.com/papers/ssrn/4649270/): The paper introduces a new stock price model based on continuous-state branching processes, providing a formula for VIX put option price.
- [Physics-informed convolutional transformer for predicting volatility surface](https://www.ml-quant.com/papers/arxiv/2209.10771/): The paper presents a new architecture using physics-informed neural networks and convolutional transformers for better predicting financial market volatility.