Fractional virus epidemic model on financial networks

Mehmet Ali Balci. "Fractional virus epidemic model on financial networks." Open Mathematics 14.1 (2016): 1074-1086. <http://eudml.org/doc/287152>.

@article{MehmetAliBalci2016,
abstract = {In this study, we present an epidemic model that characterizes the behavior of a financial network of globally operating stock markets. Since the long time series have a global memory effect, we represent our model by using the fractional calculus. This model operates on a network, where vertices are the stock markets and edges are constructed by the correlation distances. Thereafter, we find an analytical solution to commensurate system and use the well-known differential transform method to obtain the solution of incommensurate system of fractional differential equations. Our findings are confirmed and complemented by the data set of the relevant stock markets between 2006 and 2016. Rather than the hypothetical values, we use the Hurst Exponent of each time series to approximate the fraction size and graph theoretical concepts to obtain the variables.},
author = {Mehmet Ali Balci},
journal = {Open Mathematics},
keywords = {Network modelling; Stock market network; Fractional calculus; Caputo fractional derivative; Differential transform method; network modelling; stock market network; fractional calculus; differential transform method},
language = {eng},
number = {1},
pages = {1074-1086},
title = {Fractional virus epidemic model on financial networks},
url = {http://eudml.org/doc/287152},
volume = {14},
year = {2016},
}

TY - JOUR
AU - Mehmet Ali Balci
TI - Fractional virus epidemic model on financial networks
JO - Open Mathematics
PY - 2016
VL - 14
IS - 1
SP - 1074
EP - 1086
AB - In this study, we present an epidemic model that characterizes the behavior of a financial network of globally operating stock markets. Since the long time series have a global memory effect, we represent our model by using the fractional calculus. This model operates on a network, where vertices are the stock markets and edges are constructed by the correlation distances. Thereafter, we find an analytical solution to commensurate system and use the well-known differential transform method to obtain the solution of incommensurate system of fractional differential equations. Our findings are confirmed and complemented by the data set of the relevant stock markets between 2006 and 2016. Rather than the hypothetical values, we use the Hurst Exponent of each time series to approximate the fraction size and graph theoretical concepts to obtain the variables.
LA - eng
KW - Network modelling; Stock market network; Fractional calculus; Caputo fractional derivative; Differential transform method; network modelling; stock market network; fractional calculus; differential transform method
UR - http://eudml.org/doc/287152
ER -