Pricing and simulation for real estate index options: Radial basis point interpolation

2018 ◽  
Vol 500 ◽  
pp. 177-188 ◽  
Author(s):  
Pu Gong ◽  
Dong Zou ◽  
Jiayue Wang
Keyword(s):  
Real Estate ◽  
Index Options ◽  
Basis Point ◽  
Radial Basis ◽  
Point Interpolation

scholarly journals Radial Basis Point Interpolation Method with Reordering Gauss Domains for 2D Plane Problems

2014 ◽  
Vol 2014 ◽  
pp. 1-14
Author(s):  
Shi-Chao Yi ◽  
Fu-jun Chen ◽  
Lin-Quan Yao
Keyword(s):  
Interpolation Method ◽  
Computational Time ◽  
Integration Scheme ◽  
Cpu Time ◽  
Integration Schemes ◽  
Basis Point ◽  
Radial Basis ◽  
Point Interpolation Method ◽  
Point Interpolation ◽  
Gauss Points

We present novel Gauss integration schemes with radial basis point interpolation method (RPIM). These techniques define new Gauss integration scheme, researching Gauss points (RGD), and reconstructing Gauss domain (RGD), respectively. The developments lead to a curtailment of the elapsed CPU time without loss of the accuracy. Numerical results show that the schemes reduce the computational time to 25% or less in general.

Pricing European and American options by radial basis point interpolation

2015 ◽  
Vol 251 ◽  
pp. 363-377 ◽  
Author(s):  
Jamal Amani Rad ◽  
Kourosh Parand ◽  
Luca Vincenzo Ballestra
Keyword(s):  
American Options ◽  
Basis Point ◽  
Radial Basis ◽  
Point Interpolation

scholarly journals On the Factors Affecting the Accuracy and Robustness of Smoothed-Radial Point Interpolation Method

2016 ◽  
Vol 9 (1) ◽  
pp. 43-72 ◽  
Author(s):  
Abderrachid Hamrani ◽  
Idir Belaidi ◽  
Eric Monteiro ◽  
Philippe Lorong
Keyword(s):  
Radial Basis Functions ◽  
Interpolation Method ◽  
Basis Functions ◽  
Integration Scheme ◽  
Shape Parameters ◽  
Radial Point Interpolation Method ◽  
Radial Point Interpolation ◽  
Radial Basis ◽  
Point Interpolation Method ◽  
Point Interpolation

AbstractIn order to overcome the possible singularity associated with the Point Interpolation Method (PIM), the Radial Point Interpolation Method (RPIM) was proposed by G. R. Liu. Radial basis functions (RBF) was used in RPIM as basis functions for interpolation. All these radial basis functions include shape parameters. The choice of these shape parameters has been and stays a problematic theme in RBF approximation and interpolation theory. The object of this study is to contribute to the analysis of how these shape parameters affect the accuracy of the radial PIM. The RPIM is studied based on the global Galerkin weak form performed using two integration technics: classical Gaussian integration and the strain smoothing integration scheme. The numerical performance of this method is tested on their behavior on curve fitting, and on three elastic mechanical problems with regular or irregular nodes distributions. A range of recommended shape parameters is obtained from the analysis of different error indexes and also the condition number of the matrix system. All resulting RPIM methods perform very well in term of numerical computation. The Smoothed Radial Point Interpolation Method (SRPIM) shows a higher accuracy, especially in a situation of distorted node scheme.

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