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NONLINEAR DEPENDENCIES AND CHAOS IN THE EXCHANGE RATE OF THE DOLLAR
Bahram Adrangi,Mary Allender,Arjun Chatrath,Kambiz Raffiee People&Global Business Association 2008 Global Business and Finance Review Vol.13 No.1
Employing the daily broad dollar index we conduct a battery of tests for the presence of low-dimension chaos. The dollar index return series is subjected to Correlation Dimension tests, BDS tests, and tests for entropy. While we find strong evidence of nonlinear dependence in the data, the evidence is not consistent with chaos. Our test results indicate that a GARCH process explains the nonlinearities in the data. We also show that employing seasonally adjusted index series enhances the robustness of results via the existing tests for chaotic structure.
STOCK PRICE RESPONSES TO CHANGES IN GOVERNMENT BUDGET DEFICITS: EVIDENCE FROM MAJOR WORLD MARKETS
Bahram Adrangi,Mary Allender,Kambiz Raffiee 사람과세계경영학회 1998 Global Business and Finance Review Vol.3 No.2
In this study, we investigate the relationship between budget deficits and stock prices in France, Germany, Japan, and the U.S. from 1974 through 1995. The evidence from impulse response analysis and Granger causality tests shows that, in the short-run, only in the U.S. do deficit reductions have a positive effect on equity returns. However, in the long-run, tests show that equity prices in all markets are cointegrated with government budget deficits. Therefore, in the long-run, government deficits play an important role in the stability of equity markets and thus, influence the ability of firms to raise capital.
Nonlinear Dependencies And Chaos In The Exhange Rate Of The Dollar
Bahram Adrangi,Mary Allender,Arjun Chatrath,Kambiz Raffiee 사람과세계경영학회 2008 Global Business and Finance Review Vol.13 No.1
Employing the daily broad dollar index we conduct a battery of tests for the presence olfow-dimension chaos. The dollar index return series is subjected tu Correlation Dimension tests, EDS tests, and tests fi1r entropy. While wefind strong evidence ofnonlinear dependence in the data, the evidence is not consistent with chaos. Our test results indicate that a GARCH process explains the nonlinearities in the data. We also show that employing seasonally adjusted index series enhances the robustness ofresults via the existing tests for chaotic structure.