Data transfer minimization for financial derivative pricing using Monte Carlo simulation with GPU in 5G

2015 ◽  
Vol 29 (16) ◽  
pp. 2364-2374 ◽  
Author(s):  
Meikang Qiu ◽  
Diqiu Cao ◽  
Hai Su ◽  
Keke Gai
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Data Transfer ◽  
Derivative Pricing ◽  
Financial Derivative

scholarly journals A Comparative Analysis of Monte Carlo and Quasi-Monte Carlo Methods in Financial Derivative Pricing

2021 ◽  
Vol 69 (1) ◽  
pp. 1-6
Author(s):  
SM Arif Hossen ◽  
ABM Shahadat Hossain
Keyword(s):  
Monte Carlo ◽  
Comparative Analysis ◽  
Monte Carlo Methods ◽  
American Options ◽  
Financial Derivatives ◽  
Numerical Results ◽  
Derivative Pricing ◽  
Quasi Monte Carlo ◽  
Financial Derivative ◽  
Path Dependent

The main purpose of this dissertation is to study Monte Carlo (MC) and Quasi-Monte Carlo (QMC) methods for pricing financial derivatives. We estimate the Price of European as well as various path dependent options like Asian, Barrier and American options by using these methods. We also compute the numerical results by the above mentioned methods and compare them graphically as well with the help of the MATLAB Coding. Dhaka Univ. J. Sci. 69(1): 1-6, 2021 (January)

scholarly journals A SOCIAL SPIDER ALGORITHM FOR PRICING A FINANCIAL OPTION: A FORWARD APPROACH BY MONTE CARLO SIMULATION

2020 ◽  
Vol 5 (2) ◽  
Author(s):  
Igor Michel Santos Leite ◽  
Grasiele Regina Duarte ◽  
Leonardo Goliatt Da Fonseca
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Reference Values ◽  
Optimal Stopping ◽  
Risk Exposure ◽  
Social Spider ◽  
The Social ◽  
Financial Derivative ◽  
Social Spider Algorithm ◽  
Market Player

An option is a type of financial derivative that creates an opportunity for a market player to minimise your risk exposure in the negotiation. The main objective is pricing this derivative. The options classified as American is quite challenging to pricing and one of the most popular options considered in the market. This paper proposes the adoption of the Social Spider Algorithm (SSA) to optimise the parameters of the optimal-stopping with the Monte Carlo Simulation (MCS) for pricing the options classified as American. The experiments were performed using a set of options values/characteristic available in the literature. The results showed the accuracy of the combination SSA+MCS when compared with reference values.

Electron Scattering in Solids

1990 ◽  
Vol 48 (2) ◽  
pp. 4-5
Author(s):  
Ryuichi Shimizu ◽  
Ze-Jun Ding
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Angular Distribution ◽  
Elastic Scattering ◽  
Electron Scattering ◽  
Cross Sections ◽  
Expansion Method ◽  
Electron Excitation ◽  
Partial Wave Expansion ◽  
Backscattered Electrons

Monte Carlo simulation has been becoming most powerful tool to describe the electron scattering in solids, leading to more comprehensive understanding of the complicated mechanism of generation of various types of signals for microbeam analysis.The present paper proposes a practical model for the Monte Carlo simulation of scattering processes of a penetrating electron and the generation of the slow secondaries in solids. The model is based on the combined use of Gryzinski’s inner-shell electron excitation function and the dielectric function for taking into account the valence electron contribution in inelastic scattering processes, while the cross-sections derived by partial wave expansion method are used for describing elastic scattering processes. An improvement of the use of this elastic scattering cross-section can be seen in the success to describe the anisotropy of angular distribution of elastically backscattered electrons from Au in low energy region, shown in Fig.l. Fig.l(a) shows the elastic cross-sections of 600 eV electron for single Au-atom, clearly indicating that the angular distribution is no more smooth as expected from Rutherford scattering formula, but has the socalled lobes appearing at the large scattering angle.

Determination of Stoichiometry of GaAs Component in (Gaas)Ge Epitaxially Grown Thin Films by Electron Microprobe X-Ray Analysis

1990 ◽  
Vol 48 (2) ◽  
pp. 264-265
Author(s):  
D. R. Liu ◽  
S. S. Shinozaki ◽  
R. J. Baird
Keyword(s):  
Thin Film ◽  
Thin Films ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Compound Semiconductors ◽  
Energy Dispersive Analysis ◽  
X Ray ◽  
Metastable Alloys ◽  
Lower Voltage

The epitaxially grown (GaAs)Ge thin film has been arousing much interest because it is one of metastable alloys of III-V compound semiconductors with germanium and a possible candidate in optoelectronic applications. It is important to be able to accurately determine the composition of the film, particularly whether or not the GaAs component is in stoichiometry, but x-ray energy dispersive analysis (EDS) cannot meet this need. The thickness of the film is usually about 0.5-1.5 μm. If Kα peaks are used for quantification, the accelerating voltage must be more than 10 kV in order for these peaks to be excited. Under this voltage, the generation depth of x-ray photons approaches 1 μm, as evidenced by a Monte Carlo simulation and actual x-ray intensity measurement as discussed below. If a lower voltage is used to reduce the generation depth, their L peaks have to be used. But these L peaks actually are merged as one big hump simply because the atomic numbers of these three elements are relatively small and close together, and the EDS energy resolution is limited.

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