Neural networks for option pricing and hedging: a literature review
Neural networks have been used as a nonparametric method for option pricing and hedging
since the early 1990s. Far over a hundred papers have been published on this topic. This …
since the early 1990s. Far over a hundred papers have been published on this topic. This …
Volatility is rough
J Gatheral, T Jaisson, M Rosenbaum - Quantitative finance, 2018 - Taylor & Francis
Estimating volatility from recent high frequency data, we revisit the question of the
smoothness of the volatility process. Our main result is that log-volatility behaves essentially …
smoothness of the volatility process. Our main result is that log-volatility behaves essentially …
Pricing under rough volatility
From an analysis of the time series of realized variance using recent high-frequency data,
Gatheral et al.[Volatility is rough, 2014] previously showed that the logarithm of realized …
Gatheral et al.[Volatility is rough, 2014] previously showed that the logarithm of realized …
Uncertainty shocks as second-moment news shocks
We provide evidence on the relationship between aggregate uncertainty and the
macroeconomy. Identifying uncertainty shocks using methods from the news shocks …
macroeconomy. Identifying uncertainty shocks using methods from the news shocks …
A bibliographic overview of financial engineering in the emerging financial market
JR Jena, SK Biswal, AK Shrivastava… - International Journal of …, 2023 - Springer
Financial engineering is constantly changing and encountering new problems. Financial
engineering helps us detect emerging trends and challenges, such as fintech's effect on …
engineering helps us detect emerging trends and challenges, such as fintech's effect on …
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 …
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 …
Deep calibration of the quadratic rough Heston model
M Rosenbaum, J Zhang - arXiv preprint arXiv:2107.01611, 2021 - arxiv.org
The quadratic rough Heston model provides a natural way to encode Zumbach effect in the
rough volatility paradigm. We apply multi-factor approximation and use deep learning …
rough volatility paradigm. We apply multi-factor approximation and use deep learning …
Differential machine learning
B Huge, A Savine - arXiv preprint arXiv:2005.02347, 2020 - arxiv.org
Differential machine learning combines automatic adjoint differentiation (AAD) with modern
machine learning (ML) in the context of risk management of financial Derivatives. We …
machine learning (ML) in the context of risk management of financial Derivatives. We …