Sharing and Avatar-Based Innovation Tools on Digital Economy Perspectives Using Levy Processes Simulation

2020 ◽  
Vol 11 (3) ◽  
pp. 52-63
Author(s):  
Vardan Mkrttchian ◽  
Yulia Vertakova
Keyword(s):  
Process Simulation ◽  
Efficient Algorithm ◽  
Stock Price ◽  
Lévy Processes ◽  
Sharing Economy ◽  
Levy Processes ◽  
Digital Economy ◽  
Variance Gamma ◽  
R Language ◽  
Variance Gamma Process

This article is the Enhancement of the Mkrttchian and Vertakova article “Digital Sharing Economy” published in the International Journal of Innovation in Digital Economy (IJIDE, Volume 10, issue 2) and the chapter “Avatar-Based Innovation Tools for Managerial Perspectives on Digital Sharing Economy” in the book “Avatar-Based Models, Tools, and Innovation in the Digital Economy,” focused on an entirely new area relevant to the scope of IJIDE. The article discusses the capabilities of the R language for modeling Levy processes - processes that currently closely correspond to the nature of the evolution of stock price movements. The efficient algorithm of the CGMY process simulation as a difference of the tempered stable independent Levy is processed and programmed at R language. The efficient algorithm of variance gamma process simulation using variance gamma random variables is processed and programmed at R language, as Modelling in the Digital Globalization Era.

Levy Processes Simulation for Modeling in Bioinformatics

2020 ◽  
Vol 10 (1) ◽  
pp. 55-64
Author(s):  
Anna V. Kuzmina
Keyword(s):  
Process Simulation ◽  
Efficient Algorithm ◽  
Stock Price ◽  
Lévy Processes ◽  
Applied Research ◽  
Levy Processes ◽  
Gamma Process ◽  
Variance Gamma ◽  
R Language ◽  
Variance Gamma Process

This article discusses the capabilities of the R language for modeling Levy processes, processes that currently most closely correspond to the nature of the evolution of stock price movements. The efficient algorithm of the CGMY process simulation as a difference of the tempered stable independent Levy is processed and programmed with the R language. The efficient algorithm of variance gamma process simulation using variance gamma random variables is processed and programmed with the R language. The article is focused on an entirely new area relevant to the scope of the International Journal of Applied Research in Bioinformatics (IJARB).

Avatars-Based Decision Support System Using Blockchain and Knowledge Sharing for Processes Simulation

2021 ◽  
Vol 17 (1) ◽  
pp. 72-92
Author(s):  
Vardan Mkrttchian
Keyword(s):  
Human Capital ◽  
Decision Support ◽  
Knowledge Sharing ◽  
Process Simulation ◽  
Efficient Algorithm ◽  
Capital Management ◽  
Human Capital Management ◽  
Variance Gamma ◽  
R Language ◽  
Variance Gamma Process

This article is an enhancement of the chapter “About Digital Avatars for Control in Virtual Industries” in the book Big Data and Knowledge Sharing in Virtual Organizations. The article discusses the capabilities of the R language for modeling Levy processes that currently most closely correspond to the nature of the organizational learning movements in sliding mode. The efficient algorithm of the CGMY process simulation as a difference of the tempered stable independent Levy is processed and programmed at R language. The efficient algorithm of variance gamma process simulation using variance gamma random variables is processed and programmed at R language. Overview of CGMY process simulation in practice is use for human capital management in the context of the implementation of digital intelligent decision support systems and knowledge management and for digital intelligent design of avatar-based control with application to human capital management.

VARIANCE GAMMA PROCESS WITH MONTE CARLO SIMULATION AND CLOSED FORM APPROACH FOR EUROPEAN CALL OPTION PRICE DETERMINATION

2021 ◽  
Vol 14 (2) ◽  
pp. 183-193
Author(s):  
Abdul Hoyyi ◽  
Abdurakhman Abdurakhman ◽  
Dedi Rosadi
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Closed Form ◽  
Stock Price ◽  
Option Price ◽  
Secondary Data ◽  
Exponential Model ◽  
Variance Gamma ◽  
Variance Gamma Process ◽  
Normally Distributed

The Option is widely applied in the financial sector.  The Black-Scholes-Merton model is often used in calculating option prices on a stock price movement. The model uses geometric Brownian motion which assumes that the data is normally distributed. However, in reality, stock price movements can cause sharp spikes in data, resulting in nonnormal data distribution. So we need a stock price model that is not normally distributed. One of the fastest growing stock price models today is the  process exponential model. The  process has the ability to model data that has excess kurtosis and a longer tail (heavy tail) compared to the normal distribution. One of the members of the  process is the Variance Gamma (VG) process. The VG process has three parameters which each of them, to control volatility, kurtosis and skewness. In this research, the secondary data samples of options and stocks of two companies were used, namely zoom video communications, Inc. (ZM) and Nokia Corporation (NOK).  The price of call options is determined by using closed form equations and Monte Carlo simulation. The Simulation was carried out for various  values until convergent result was obtained.

scholarly journals Memory-Reduction Method for Pricing American-Style Options under Exponential Lévy Processes

2011 ◽  
Vol 1 (1) ◽  
pp. 20-34 ◽  
Author(s):  
Raymond H. Chan ◽  
Tao Wu
Keyword(s):  
Lévy Processes ◽  
Computational Cost ◽  
Simulation Method ◽  
Levy Processes ◽  
Storage Requirement ◽  
Variance Gamma Process ◽  
The Monte Carlo Method ◽  
Exponential Lévy Models ◽  
American Style ◽  
Memory Reduction

AbstractThis paper concerns the Monte Carlo method in pricing American-style options under the general class of exponential Lévy models. Traditionally, one must store all the intermediate asset prices so that they can be used for the backward pricing in the least squares algorithm. Therefore the storage requirement grows like , where m is the number of time steps and n is the number of simulated paths. In this paper, we propose a simulation method where the storage requirement is only . The total computational cost is less than twice that of the traditional method. For machines with limited memory, one can now enlarge m and n to improve the accuracy in pricing the options. In numerical experiments, we illustrate the efficiency and accuracy of our method by pricing American options where the log-prices of the underlying assets follow typical Lévy processes such as Brownian motion, lognormal jump-diffusion process, and variance gamma process.

On correlating Lévy processes

2010 ◽  
Vol 13 (1) ◽  
pp. 3-16 ◽  
Author(s):  
Ernst Eberlein ◽  
Dilip Madan
Keyword(s):  
Lévy Processes ◽  
Levy Processes
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