Mingqian Zhang
Research Subjects
Mingqian Zhang has a total of 5 co-authors. Here are the top co-authors by number of shared publications:
- Ruosong Cao
School of Mathematics and Statistics, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China
1 shared publication - Tao Liu
College of Urban and Environmental Sciences Peking University Beijing China
1 shared publication - Xiaoxi Hu
Tsinghua University
1 shared publication - Yakun Li
School of Economics and Management, Yanshan University, Qinhuangdao 066000, China
1 shared publication - Yutong Li
School of Mathematics and Statistics, Northeastern University at Qinhuangdao, Qinhuangdao 066004, China
1 shared publication
Latest Publications
- Open Access
Fractional Black–Scholes Under Memory Effects: A Sixth-Order Local RBF–FD Scheme with Integrated Multiquadric Kernels
In this work, a high-order meshless framework is developed for the numerical resolution of the temporal–fractional Black–Scholes equation arising in option pricing with long-memory effects. The spatial discretization is carried out with a local radial basis function produced finite difference (RBF–FD) method on seven-node stencils. Analytical differentiation weights are constructed by employing closed-form second integrations of a variant of the inverse multiquadric kernel, which yields sparse differentiation matrices. Explicit formulas are derived for both first- and second-order operators, and a detailed truncation error analysis confirms sixth-order convergence in space. Numerical experiments for European options discuss better accuracy per spatial node than standard finite difference schemes.
Open AccessFractional Black–Scholes Under Memory Effects: A Sixth-Order Local RBF–FD Scheme with Integrated Multiquadric Kernels
In this work, a high-order meshless framework is developed for the numerical resolution of the temporal–fractional Black–Scholes equation arising in option pricing with long-memory effects. The spatial discretization is carried out with a local radial basis function produced finite difference (RBF–FD) method on seven-node stencils. Analytical differentiation weights are constructed by employing closed-form second integrations of a variant of the inverse multiquadric kernel, which yields sparse differentiation matrices. Explicit formulas are derived for both first- and second-order operators, and a detailed truncation error analysis confirms sixth-order convergence in space. Numerical experiments for European options discuss better accuracy per spatial node than standard finite difference schemes.