scholarly journals A Comparison of Artificial Neural Networks and Bootstrap Aggregating Ensembles in a Modern Financial Derivative Pricing Framework

2021 ◽  
Vol 14 (6) ◽  
pp. 254
Author(s):  
Ryno du Plooy ◽  
Pierre J. Venter
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Implied Volatility ◽  
Stock Exchange ◽  
Learning Networks ◽  
Bootstrap Aggregating ◽  
Artificial Neural ◽  
Pricing Framework

In this paper, the pricing performances of two learning networks, namely an artificial neural network and a bootstrap aggregating ensemble network, were compared when pricing the Johannesburg Stock Exchange (JSE) Top 40 European call options in a modern option pricing framework using a constructed implied volatility surface. In addition to this, the numerical accuracy of the better performing network was compared to a Monte Carlo simulation in a separate numerical experiment. It was found that the bootstrap aggregating ensemble network outperformed the artificial neural network and produced price estimates within the error bounds of a Monte Carlo simulation when pricing derivatives in a multi-curve framework setting.

scholarly journals Investigating the Electronic Coefficients in Ne-Hg Mixtures: Comparing a Monte Carlo and Artificial Neural Network Model

2020 ◽  
Vol 55 (2) ◽  
Author(s):  
Rafid Abbas Ali ◽  
Faten Sajet Mater ◽  
Asmaa Satar Jeeiad Al-Ragehey
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Network Architecture ◽  
Vapor Concentration ◽  
Neural Network Architecture ◽  
Marquardt Algorithm ◽  
Artificial Neural ◽  
New Applications

Electron coefficients such as drift velocity, ionization coefficient, mean electron energy and Townsend energy for different concentrations of Hg 0.1%, 1%, 10% and 50% in the Ne-Hg mixture at a reduced electric field were calculated using two approaches taking into account inelastic collisions: The Monte Carlo simulation, and an artificial neural network. The effect of Hg vapor concentration on the electron coefficients showed that insignificant additions of mercury atom impurities to Neon, starting from fractions of a percent, affect the characteristics of inelastic processes and discharge, respectively. The aim of this paper is to explore the new applications of neural networks. The Levenberg-Marquardt algorithm and artificial neural network architecture employed was presented in this work to calculate the electron coefficients for different concentrations of Hg in Ne-Hg mixtures. The artificial neural network has been trained with four models (M1, M2, M3, M4), and analysis of the regression between the values of an artificial neural network and Monte Carlo simulation indicates that the M2 output provided the best perfect correlation at 100 Epochs, and the output data obtained was closest to the target data required through using the different stages of artificial neural network development starting with design, training and testing.

Monte Carlo Simulation and Artificial Neural Network Modeling of Ferro-and Antiferro-Transition Behavior in Two Dimensional Binary Alloy

2013 ◽  
Vol 431 ◽  
pp. 61-65
Author(s):  
Wimalin Laosiritaworn ◽  
Yongyut Laosiritaworn
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Binary Alloy ◽  
Domain Size ◽  
Neural Network Modeling ◽  
Two Dimensional ◽  
Data Mining Technique ◽  
Artificial Neural

This work investigated the competition effect between the ferro-and antiferro-interaction on the domain size and domain interface in two-dimensional binary alloy. Monte Carlo simulation and Ising model were used to model the alloy system where largest domain size and the domain interface were observed to identify the low temperature ordered phase and the high temperature disordered phase. The simulation results show that domain size is maximized when the ferro-interaction is preferred, but domain interface becomes maximum instead when the antiferro-interaction is favored. These domain properties were reported as a function of temperature for various magnitude of ferro-and antiferro-interactions. In addition, the artificial neural network was used to create database of relationship among the ferro-and antiferro-interaction, the simulated temperature and the domain properties. Good agreement between the real targeted outputs and the predicted outputs was found, which confirm the learning-by-example ability of the artificial neural network. This work therefore presents another step in the understanding of how complex interaction plays its role in binary alloy problem and how a data mining technique assists development of understanding in materials science problems.

Application of Artificial Neural Network to Calculate Curie Temperature of Ferromagnetic Materials

2008 ◽  
Vol 55-57 ◽  
pp. 901-904 ◽  
Author(s):  
Wimalin S. Laosiritaworn
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Curie Temperature ◽  
Technological Development ◽  
Ferromagnetic Materials ◽  
Ising Spin ◽  
Simulated System ◽  
Artificial Neural

Ferromagnetic materials are now interested by researchers as they have applications in various industries. Due to the complexity of the materials, an important contribution to enhance the technological development has come from the theoretical and simulation studies especially from the Monte Carlo simulation. Nevertheless, the Monte Carlo is often limited in its performance because the computational limitations, such as the simulated system sizes and simulation times. These limitations also put a constraint on the simulation time which caps the numerical accuracy. The artificial neural network is used in this study in cooperating with the Monte Carlo simulation. The aim is to investigate the possibility in obtaining the Curie temperature of ferromagnetic Ising spin in a fine scale without an intense computational required. From the results, the extracted Curie temperature is found to agree well with those from the exact theoretical analysis which verifies the artificial neural network to be a very useful technique.

scholarly journals Uncertainty Quantification in Natural Frequency of Composite Plates - An Artificial Neural Network Based Approach

2016 ◽  
Vol 25 (2) ◽  
pp. 096369351602500 ◽  
Author(s):  
Sudip Dey ◽  
Tanmoy Mukhopadhyay ◽  
Axel Spickenheuer ◽  
Uwe Gohs ◽  
S. Adhikari
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Uncertainty Quantification ◽  
Natural Frequency ◽  
Composite Plates ◽  
Ann Model ◽  
Artificial Neural ◽  
Natural Frequency Analysis

This paper presents the stochastic natural frequency for laminated composite plates by using artificial neural network (ANN) model. The ANN model is employed as a surrogate and is trained by using Latin hypercube sampling. Subsequently the stochastic first two natural frequencies are quantified with ANN based uncertainty quantification algorithm. The convergence of the proposed algorithm for stochastic natural frequency analysis of composite plates is verified and validated with original finite element method (FEM) in conjunction with Monte Carlo simulation. Both individual and combined variation of stochastic input parameters are considered to address the influence on the output of interest. The sample size and computational cost are reduced by employing the present approach compared to traditional Monte Carlo simulation.

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