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NONLINEAR DEPENDENCIES IN CURRENCY FUTURES
Bahram Adrangi,Arjun Chatrath People&Global Business Association 1999 Global Business and Finance Review Vol.4 No.2
Several studies have documented nonlinear dependencies in the exchange rates of major currencies. This paper provides similar evidence for the currency futures of the British Pound, Deutsche Mark, Swiss Franc, Canadian dollar, and Japanese Yen. It is established that the GARCH (1,1) model satisfactorily explains the nonlinear dependencies in the contracts investigated. Neither trading-volume/open-interest, nor the time to maturity or the basis are found to explain the GARCH effects in the data. However, the conditional volatility in the currency futures' is positively related to futures trading activity and the basis. Finally, we find no support for Samuelson's maturity hypothesis.
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.
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.