The statistical distribution of exchange rates: Empirical evidence and economic implications

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The abundant evidence that changes in exchange rates have distributions with fatter tails than the normal distribution has led researchers to consider non-normal distributions, including the Student, the stable Paretian and mixtures of distributions. This paper compares the empirical fits of three non-normal candidates and the normal distribution for daily changes in the logarithms of exchange rates, using maximum likelihood estimation of the parameters and chi-square goodness-of-fit tests. The Student and mixture of two normals provide the best fits, but there is evidence that the distribution parameters may vary over time.

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      All currency data were obtained from the DataStream database. This study is motivated by substantial evidence of complex intertemporal and interstructural dynamics (Hollist, 1981) in the modeling of exchange rate returns (Alexander and Lazar, 2006; Boothe and Glassman, 1987; Huisman et al., 2002). Five primary issues are taken into consideration in the specification of the proposed econometric model: (i) multivariate interaction among the three selected currencies and their associated markets in each region; (ii) asymmetry in the volatility dynamics of exchange rates; (iii) asymmetry in exchange rate correlation processes; (iv) allowance for a structural break in the mean, variance and correlation structures resulting from the global economic crisis; and (v) potential non-normality in exchange rate regression errors.

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    We gratefully acknowledge financial support from the Social Science and Humanities Research Council of Canada. We wish to thank an anonymous referee for helpful comments and Shirley Haun for research assistance. Special thanks to John Petkau of the University of British Columbia Statistical Consulting and Research Laboratory for statistical advice.

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