---
title: Modelling the Index Option Smirk in China: Do Non-Affine Two-Factor Stochastic Volatility Models Work?
url: https://www.ml-quant.com/papers/ssrn/4698894/
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 4698894
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4698894
featured: 2024-01-23
citations: 0
topic: Derivatives & Volatility
---


# Modelling the Index Option Smirk in China: Do Non-Affine Two-Factor Stochastic Volatility Models Work?

A new multifactor stochastic volatility model for the Chinese options market surpasses the double Heston model in option pricing performance and correlation structure.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4698894
- Identifier: SSRN 4698894
- Released: 2024-01-18
- First featured: Quant Letter No. 34 (2024-01-23): https://www.ml-quant.com/issues/2024-01-23/
- Citations (Semantic Scholar): 0
- Published in: not yet
- Topic: Derivatives & Volatility

## Related

- [Data-driven Option Pricing](https://www.ml-quant.com/papers/arxiv/2401.11158/): A new data-driven option pricing method is suggested, using historical asset prices and deep learning to solve optimization problems, proving effective in numerical tests.
- [Consistent asset modelling with random coefficients and switches between regimes](https://www.ml-quant.com/papers/arxiv/2401.09955/): A stochastic model is examined that incorporates external influences and uncertainty in the parametrization of stochastic dynamics, using a Markov-modulated approach for regime switching, applied to option pricing in a numerical experiment.
- [A Two-Step Longstaff Schwartz Monte Carlo Approach to Game Option Pricing](https://www.ml-quant.com/papers/arxiv/2401.08093/): The article suggests a two-step Longstaff Schwartz Monte Carlo method for pricing game options, which provides more reliable results than the original method.
- [A deep implicit-explicit minimizing movement method for option pricing in jump-diffusion models](https://www.ml-quant.com/papers/arxiv/2401.06740/): The paper introduces a deep learning method for pricing European basket options using Artificial Neural Networks and two methods for discretizing the integral operator, focusing on assets with jump-diffusion dynamics.
- [Higher order approximation of option prices in Barndorff-Nielsen and Shephard models](https://www.ml-quant.com/papers/arxiv/2401.14390/): The paper introduces an approximation method for pricing European options in Barndorff-Nielsen and Shephard models, utilizing a recursive algorithm for closed form option price approximations.
- [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.