114 citations · 185 across the 11 of their papers we have counts for
11 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.…
Optimal Market Making in the Chinese Stock Market: A Stochastic Control and Scenario Analysis
Shiqi Gong, Shuaiqiang Liu, Danny D. Sun
Market making plays a crucial role in providing liquidity and maintaining stability in financial markets, making it an essential component of well-functioning capital markets. Desp…
GPU acceleration of the Seven-League Scheme for large time step simulations of stochastic differential equations
Shuaiqiang Liu, Graziana Colonna, Lech A. Grzelak +1
Monte Carlo simulation is widely used to numerically solve stochastic differential equations. Although the method is flexible and easy to implement, it may be slow to converge. Mor…
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…
The Seven-League Scheme: Deep learning for large time step Monte Carlo simulations of stochastic differential equations
Shuaiqiang Liu, Lech A. Grzelak, Cornelis W. Oosterlee
We propose an accurate data-driven numerical scheme to solve Stochastic Differential Equations (SDEs), by taking large time steps. The SDE discretization is built up by means of a…
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…