smq/directoryAn evolving quant resource archive
Paper / 2025SMQ / 277791112726085
An Efficient Calibration Framework for Volatility Derivatives under Rough Volatility with Jumps
derivatives / machine learningResource overview

An Efficient Calibration Framework for Volatility Derivatives under Rough Volatility with Jumps

Paper2025

by Keyuan Wu, Tenghan Zhong, Yuxuan Ouyang

Tags
derivativesmachine learningmodel calibrationoption pricingrough volatility

Continue on SelfMadeQuant

Understand this paper

Suggested foundations for optimisation and numerical methods and volatility models and surfaces. Specialist prerequisites vary by paper.

Before you read

Go further

Explore a related reading path