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Found 8 papers, 1 papers with code
Rough PDEs for local stochastic volatility models
In this work, we introduce a novel pricing methodology in general, possibly non-Markovian local stochastic volatility (LSV) models.
Reconstructing Volatility: Pricing of Index Options under Rough Volatility
In previous works Avellaneda et al. pioneered the pricing and hedging of index options - products highly sensitive to implied volatility and correlation assumptions - with large deviations methods, assuming local volatility dynamics for all components of the index.
Weak error estimates for rough volatility models
We consider a class of stochastic processes with rough stochastic volatility, examples of which include the rough Bergomi and rough Stein-Stein model, that have gained considerable importance in quantitative finance.
Local volatility under rough volatility
Several asymptotic results for the implied volatility generated by a rough volatility model have been obtained in recent years (notably in the small-maturity regime), providing a better understanding of the shapes of the volatility surface induced by rough volatility models, and supporting their calibration power to S&P500 option data.
Stability of Deep Neural Networks via discrete rough paths
Using rough path techniques, we provide a priori estimates for the output of Deep Residual Neural Networks in terms of both the input data and the (trained) network weights.
Unified Signature Cumulants and Generalized Magnus Expansions
The signature of a path can be described as its full non-commutative exponential.
Probability 60L10, 60L90, 60E10, 60G44, 60G48, 60G51, 60J76
Short dated smile under Rough Volatility: asymptotics and numerics
2021] we introduce a new methodology to analyze large classes of (classical and rough) stochastic volatility models, with special regard to short-time and small noise formulae for option prices, using the framework [Bayer et al; A regularity structure for rough volatility; Math.
Precise asymptotics: robust stochastic volatility models
We present a new methodology to analyze large classes of (classical and rough) stochastic volatility models, with special regard to short-time and small noise formulae for option prices.
