1 citations · 1 across the 6 of their papers we have counts for
7 papers
RIDGE: An Autonomous Framework for Validation and Method Discovery in LLM-Generated Option Pricing
Liexin Cheng, Xue Cheng, Shuaiqiang Liu +1
Automated code generation is becoming an important tool in quantitative finance, where large language models can generate option pricing implementations directly from mathematical…
Fast Learning in Quantitative Finance with Extreme Learning Machine
Liexin Cheng, Xue Cheng, Shuaiqiang Liu
A critical factor in adopting machine learning for time-sensitive financial tasks is computational speed, including model training and inference. This paper demonstrates that a bro…
Improved model-free bounds for multi-asset options using option-implied information and deep learning
Evangelia Dragazi, Shuaiqiang Liu, Antonis Papapantoleon
We consider the computation of model-free bounds for multi-asset options in a setting that combines dependence uncertainty with additional information on the dependence structure.…
Solution of integrals with fractional Brownian motion for different Hurst indices
Fei Gao, Shuaiqiang Liu, Cornelis W. Oosterlee +1
In this paper, we will evaluate integrals that define the conditional expectation, variance and characteristic function of stochastic processes with respect to fractional Brownian…
Monte Carlo Simulation of SDEs using GANs
Jorino van Rhijn, Cornelis W. Oosterlee, Lech A. Grzelak +1
Generative adversarial networks (GANs) have shown promising results when applied on partial differential equations and financial time series generation. We investigate if GANs can…
On Calibration Neural Networks for extracting implied information from American options
Shuaiqiang Liu, Álvaro Leitao, Anastasia Borovykh +1
Extracting implied information, like volatility and/or dividend, from observed option prices is a challenging task when dealing with American options, because of the computational…