Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Liu, Shuaiqiang"'
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. More specifically, we consider the setting where the marginal distribu
Externí odkaz:
http://arxiv.org/abs/2404.02343
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. Despite its importance, there is limited research on market making in the
Externí odkaz:
http://arxiv.org/abs/2306.02764
Publikováno v:
Chapter in Mathematics: Key Enabling Technology for Scientific Machine Learning by NDNS+, 2021 Cluster
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. Moreover, an inaccurate solution will result when using large time steps.
Externí odkaz:
http://arxiv.org/abs/2302.05170
Autor:
Liu, Shuaiqiang, Li, Yu, Yue, Yan, Jiang, Xingxing, Ding, Chuanmin, Wang, Junwen, Duan, Donghong, Yuan, Qinbo, Hao, Xiaogang, Liu, Shibin
Publikováno v:
In International Journal of Hydrogen Energy 4 October 2024 85:252-260
Autor:
Li, Yu, Du, Xiao, Yuan, Qinbo, Wang, Junwen, Liu, Shuaiqiang, Ding, Chuanmin, Hao, Xiaogang, Liu, Shibin
Publikováno v:
In Journal of Energy Storage 1 October 2024 99 Part A
In this paper, we will evaluate integrals that define the conditional expectation, variance and characteristic function of stochastic processes with respect to fractional Brownian motion (fBm) for all relevant Hurst indices, i.e. $H \in (0,1)$. The f
Externí odkaz:
http://arxiv.org/abs/2203.02323
Generative adversarial networks (GANs) have shown promising results when applied on partial differential equations and financial time series generation. We investigate if GANs can also be used to approximate one-dimensional Ito stochastic differentia
Externí odkaz:
http://arxiv.org/abs/2104.01437
Autor:
Liu, Shuaiqiang, Li, Yu, Yue, Yan, Yang, Huazhao, Ding, Chuanmin, Wang, Junwen, Duan, Donghong, Yuan, Qinbo, Hao, Xiaogang, Liu, Shibin
Publikováno v:
In International Journal of Hydrogen Energy 22 March 2024 60:425-433
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 polynomial chaos expansion method, on the basis of accurately determin
Externí odkaz:
http://arxiv.org/abs/2009.03202
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 costs needed to solve the corresponding mathematical problem many thou
Externí odkaz:
http://arxiv.org/abs/2001.11786