Parametric Distributional Flexibility and Conditional Variance Models with an Application to Hourly Exchange Rates

39 Pages Posted: 15 Feb 2006

See all articles by Jenny N. Lye

Jenny N. Lye

University of Melbourne - Department of Economics

Vance L. Martin

University of Melbourne - Department of Economics; Australian National University (ANU) - Centre for Applied Macroeconomic Analysis (CAMA)

Leslie Teo

International Monetary Fund (IMF)

Date Written: March 1998

Abstract

This paper builds on the ARCH approach for modeling distributions with time-varying conditional variance by using the generalized Student t distribution. The distribution offers flexibility in modeling both leptokurtosis and asymmetry (characteristics seen in high-frequency financial time series data), nests the standard normal and Student t distributions, and is related to the Gram Charlier and mixture distributions. An empirical ARCH model based on this distribution is formulated and estimated using hourly exchange rate returns for four currencies. The generalized Student t is found to better model the empirical conditional and unconditional distributions than other distributional specifications.

Keywords: ARCH, Generalized Student t Distributions, Modeling Variance, Exchange Rates

JEL Classification: C10, C50, F31

Suggested Citation

Lye, Jenny N. and Martin, Vance L. and Teo, Leslie, Parametric Distributional Flexibility and Conditional Variance Models with an Application to Hourly Exchange Rates (March 1998). IMF Working Paper No. 98/29, Available at SSRN: https://ssrn.com/abstract=882265

Jenny N. Lye (Contact Author)

University of Melbourne - Department of Economics ( email )

Melbourne, 3010
Australia

Vance L. Martin

University of Melbourne - Department of Economics ( email )

Melbourne, 3010
Australia

Australian National University (ANU) - Centre for Applied Macroeconomic Analysis (CAMA)

ANU College of Business and Economics
Canberra, Australian Capital Territory 0200
Australia

Leslie Teo

International Monetary Fund (IMF) ( email )

700 19th Street NW
Washington, DC 20431
United States

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