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dc.contributor.authorChayko, Olesia
dc.contributor.authorMorozova, Vera
dc.date.accessioned2012-10-31T12:54:52Z
dc.date.available2012-10-31T12:54:52Z
dc.date.issued2012
dc.identifier.urihttp://hdl.handle.net/11250/183938
dc.descriptionMaster's thesis in Financeno_NO
dc.description.abstractReoccurring financial and macroeconomic disasters consequences of which lead to greater financial costs and hinder a healthy market functioning in the world’s economy, need to be paid a special attention to in terms of understanding their nature and the ways to hedge them.We base our empirical study on long term data including 42 countries for GDP, consumption, real exchange rates, net import, long term government bond yield, stock price indices and inflaton. The countries are organized in three main groups: Global, OECD and non-OECD and then split in six groups on the basis of continents division. First, we reveal the essential data chactericstics using descriptive statistics analysis. Then, with the help of correlation analysis we detect statistically significant relationships between the variables for each country category. The purpose is to establish the interaction process between macroeconomic and financial factors. Further, we perform the logistic regression analysis with binary codes for both dependent and independent variables in order to establish the best predictor models. The purpose is to discover whether annual growths in some variable would lead to increase/decrease in real pc GDP. We document that the best prediction ability is revealed by consumption on GDP, though for some country categories other best predictors are detected. These include stock price indices, inflation, long term government bond yield, net import and real exchange rates for various categories. Potential future research involves data modification in terms of collecting data of higher frequency, constructing missing data gaps, and forming binary variables for logistic regression analysis on the basis of improved crisis thresholds.no_NO
dc.language.isoengno_NO
dc.publisherUniversity of Stavanger, Norwayno_NO
dc.relation.ispartofseriesMasteroppgave/UIS-SV-HH/2012;
dc.subjectextreme eventsno_NO
dc.subjectdisasterno_NO
dc.subjectheavy tailsno_NO
dc.subjectlogistic regressionno_NO
dc.subjectøkonomino_NO
dc.subjectadministrasjonno_NO
dc.subjectanvendt finansno_NO
dc.titleExtreme events in the stock market and economyno_NO
dc.typeMaster thesisno_NO
dc.subject.nsiVDP::Social science: 200::Economics: 210no_NO


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