Technical paper/Rough volatility
The power of neural networks in stochastic volatility modeling
The authors apply stochastic volatility models to real-world data and demonstrate how effectively the models calibrate a range of options.
Efficient simulation of affine forward variance models
Andersen's quadratic-exponential scheme is used for simulations of rough volatility models
Dynamically controlled kernel estimation
An accurate data-driven and model-agnostic method to compute conditional expectations is presented
The quadratic rough Heston model and the joint S&P 500/Vix smile calibration problem
A combination of rough volatility and price-feedback effect allows for SPX-Vix joint calibration
The joint S&P 500/Vix smile calibration puzzle solved
SPX and Vix derivatives are modelled jointly in an arbitrage-free setting
ADOL: Markovian approximation of a rough lognormal model
A variation of the rough volatility model is introduced by plugging in a different stochastic process
Roughening Heston
El Euch, Rosenbaum, Gatheral combine a rough volatility model with the classical Heston model