Deep learning volatility: a deep neural network perspective on pricing and calibration in (rough) volatility models
We present a neural network-based calibration method that performs the calibration task
within a few milliseconds for the full implied volatility surface. The framework is consistently …
within a few milliseconds for the full implied volatility surface. The framework is consistently …
On deep calibration of (rough) stochastic volatility models
Techniques from deep learning play a more and more important role for the important task of
calibration of financial models. The pioneering paper by Hernandez [Risk, 2017] was a …
calibration of financial models. The pioneering paper by Hernandez [Risk, 2017] was a …
Joint SPX-VIX calibration with Gaussian polynomial volatility models: deep pricing with quantization hints
EA Jaber, C Illand - arXiv preprint arXiv:2212.08297, 2022 - arxiv.org
We consider the joint SPX-VIX calibration within a general class of Gaussian polynomial
volatility models in which the volatility of the SPX is assumed to be a polynomial function of a …
volatility models in which the volatility of the SPX is assumed to be a polynomial function of a …
Empirical analysis of rough and classical stochastic volatility models to the SPX and VIX markets
SE Rømer - Quantitative Finance, 2022 - Taylor & Francis
We conduct an empirical analysis of rough and classical stochastic volatility models to the
SPX and VIX options markets. Our analysis focusses primarily on calibration quality and is …
SPX and VIX options markets. Our analysis focusses primarily on calibration quality and is …
Deep learning volatility
We present a neural network based calibration method that performs the calibration task
within a few milliseconds for the full implied volatility surface. The framework is consistently …
within a few milliseconds for the full implied volatility surface. The framework is consistently …
Statistical inference for rough volatility: Minimax theory
Statistical inference for rough volatility: Minimax theory Page 1 The Annals of Statistics 2024,
Vol. 52, No. 4, 1277–1306 https://doi.org/10.1214/23-AOS2343 © Institute of Mathematical …
Vol. 52, No. 4, 1277–1306 https://doi.org/10.1214/23-AOS2343 © Institute of Mathematical …
Short-time near-the-money skew in rough fractional volatility models
We consider rough stochastic volatility models where the driving noise of volatility has
fractional scaling, in the 'rough'regime of Hurst parameter H< 1/2. This regime recently …
fractional scaling, in the 'rough'regime of Hurst parameter H< 1/2. This regime recently …
[图书][B] Rough volatility
Since we will never really know why the prices of financial assets move, we should at least
make a faithful model of how they move. This was the motivation of Bachelier in 1900, when …
make a faithful model of how they move. This was the motivation of Bachelier in 1900, when …
Optimal stopping with signatures
We propose a new method for solving optimal stopping problems (such as American option
pricing in finance) under minimal assumptions on the underlying stochastic process X. We …
pricing in finance) under minimal assumptions on the underlying stochastic process X. We …