1 citations · 3 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
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…
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…
A neural network-based framework for financial model calibration
Shuaiqiang Liu, Anastasia Borovykh, Lech A. Grzelak +1
A data-driven approach called CaNN (Calibration Neural Network) is proposed to calibrate financial asset price models using an Artificial Neural Network (ANN). Determining optimal…
Pricing options and computing implied volatilities using neural networks
Shuaiqiang Liu, Cornelis W. Oosterlee, Sander M. Bohte
This paper proposes a data-driven approach, by means of an Artificial Neural Network (ANN), to value financial options and to calculate implied volatilities with the aim of acceler…