MODELLING OF SCENARIOS OF THE CRISIS PHENOMENA TRANSFER AMONG FINANCIAL MARKETS

• Main Author: Inna Strelchenko
• Format: Article
• Language: English
• Published: Izdevnieciba “Baltija Publishing” 2017-11-01
• Series: Baltic Journal of Economic Studies
• Subjects: crisis; contagion; macroeconomic indicator; clustering; neural network; Kohonen map; radial basis network
• Online Access: http://www.baltijapublishing.lv/index.php/issue/article/view/187/193

The phenomenon of crisis transference among financial markets in different countries is especially evident during the global financial crisis of 2007-2009. Abnormal imbalances emerged in the market of secondary financial instruments in the United States in the second half of 2006 and quickly spread to the financial markets of most countries of the world. However, the rate of fall of the main macroeconomic indicators, the duration of the latent period (the time between the date of the beginning of the financial crisis in the source country and date of the recorded fall in GDP of the country that is subjected to “contagion” (Strelchenko, 2016), and recovery period are substantially different. To generate an effective economic policy actually, there is a task of determining the possible scenarios of transferring crisis. The research subject is a process of transfer of the crisis phenomena among the financial markets of countries with different levels of economic development. Methodology. The paper presents the results of a study on the differentiation of the financial markets reactions to the crisis transfer. To build the corresponding classification model, self-organization Kohonen neural networks are used. The purpose of this work is to build a neural network model for clustering economies according to the response to external financial shocks. This model allows predicting the scenarios of transferring crisis among financial markets. Conclusion. As a result of the study, there is built a neural network with the architecture of the Kohonen map. The neural network has one hidden layer consisting of six neurons and has a hexagonal structure. Six clusters describe six possible scenarios of the economy dynamics under the impact of the transfer of crises. Cluster number one and two unite countries characterized by a short period of economic recovery and return of the main macroeconomic indicators to the precrisis levels. A longer recovery period and high volatility in exchange rates, gross domestic product, and decline in export-import operations characterize the third and fourth clusters of SOM. As for the countries that were in the last two clusters (including Ukraine), then the result of the crisis phenomena transfer is that the average amplitude of the fall in macroeconomic indicators exceeded 15% for the sixth cluster, and 9% for cluster number 5.