The Combination of Topology and Nodes’ States Dynamics as an Early-warning Signal of Critical Transition in a Banking Network Model

Guleva, Valentina Y.

doi:10.1016/j.procs.2016.05.436

Cited by 7 publications

(2 citation statements)

References 5 publications

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“…Other research related to early warning systems and banking crises was done by Hamdaoui (2016), Lang and Schmidt (2016), Caggiano, Calice, Leonida, and Kapetanios (2016), Dabrowski, Beyers, and de Villiers (2016), Guleva (2016), Stolbov (2015), and Vermeulen et al (2015). Hamdaoui (2016, p. 114) suggests the use of a multinomial logit model based on Bayesian Model Averaging instead of conventional multinomial and binary models The empirical results of his research show that for a set of 49 developing and developed countries, the model would have correctly predicted the vast majority of crises.…”

Section: Figure (1) Rupiah Exchange Rate and Economy Development 1996mentioning

confidence: 99%

Early Warning to Banking Crises in the Dual Financial System in Indonesia: The Markov Switching Approach

Nurfalah¹,

Rusydiana²,

Laila³

et al. 2018

JKAU Islamic Econ

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This research aims at detecting early indicators that cause conventional banking and Islamic banking crises, identifying the longest crisis period for both types of banks, and comparing the stability between the two kinds of banks. The method used is the Markov Switching Vector Autoregressive (MS-VAR) approach. This study uses secondary data obtained from official sources and in monthly form from January 2004 to March 2017. The results show that Islamic banking is more stable against internal and external shocks than conventional banking. Z-score for Islamic banking is higher (11,933) than the Z-score for conventional banking (11,679). The longest crisis period for conventional banking was around April 2013 – March 2017 while for Islamic banking was around January 2008 – October 2008.

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Section: Figure (1) Rupiah Exchange Rate and Economy Development 1996mentioning

confidence: 99%

Early Warning to Banking Crises in the Dual Financial System in Indonesia: The Markov Switching Approach

Nurfalah¹,

Rusydiana²,

Laila³

et al. 2018

JKAU Islamic Econ

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show abstract

“…e next state of a process depends on the current state, not the previous state, according to the Markov model. e initial state of the entire process has nothing to do with the previous Markov process, but there is a probability in a Markov process that can be calculated from the previous state in the transition process between every two states [8]. e Markov model can obtain the corresponding Markov model by using the gathered training samples for adaptive learning.…”

Section: Introductionmentioning

confidence: 99%

Construction of Early Warning Mechanism of University Education Network Based on the Markov Model

You

2022

Mobile Information Systems

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This paper proposes and builds an early warning mechanism model of the college education network using the Markov model. This paper proposes a method to determine the observation value of Markov model based on the flow control principle and TCP/IP model in an effort to address the issue that the observation value of Markov model is challenging to determine when it is applied to intrusion detection. The detection model also employs an adaptive sliding detection window algorithm to further increase the system’s detection rate. The mechanism developed in this paper is compared to other early warning mechanisms in order to confirm the validity and applicability of the educational network early warning mechanism. The experimental results demonstrate that the accuracy of the educational network early warning mechanism in this paper is higher than that of the conventional early warning mechanism, which is 9.87 percent, at up to 95.02 percent. The proposed model, however, clearly excels in terms of early warning adaptability, model fitting level, and information overload processing effectiveness. In general, this paper successfully applies the Markov model to the early warning system of the college education network. For the study of the college education network’s early warning system, it has some reference value.

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Estimation of Tipping Points for Critical and Transitional Regimes in the Evolution of Complex Interbank Network

Guleva

2020

Lecture Notes in Computer Science

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The Combination of Topology and Nodes’ States Dynamics as an Early-warning Signal of Critical Transition in a Banking Network Model

Cited by 7 publications

References 5 publications

Early Warning to Banking Crises in the Dual Financial System in Indonesia: The Markov Switching Approach

Early Warning to Banking Crises in the Dual Financial System in Indonesia: The Markov Switching Approach

Construction of Early Warning Mechanism of University Education Network Based on the Markov Model

Estimation of Tipping Points for Critical and Transitional Regimes in the Evolution of Complex Interbank Network

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