Detecting Autoregressive Conditional Heteroskedasticity in Non-Gaussian Time Series

26 Pages Posted: 28 May 2004

See all articles by Burkhard Raunig

Burkhard Raunig

Austrian National Bank - Economic Studies Division

Date Written: August 20, 2004

Abstract

In economic time series conditional heteroskedasticity and conditional non-normality may occur simultaneously. Well known examples include time series of financial returns. The present paper examines a new test for (generalized) autoregressive conditional heteroskedasticity in Monte Carlo experiments with normal, fat-tailed and/or skewed conditional distributions. In the experiments the size of the new test is accurate and the test has good power against the considered ARCH and GARCH alternatives. Under conditional normality the test is as powerful as the standard Lagrange multiplier test for ARCH effects and a robust ARCH test. Under conditional non-normality the new test has often substantially more power then the other two tests.

Keywords: ARCH, GARCH, hypothesis testing

JEL Classification: C12, C14, C22, C52

Suggested Citation

Raunig, Burkhard, Detecting Autoregressive Conditional Heteroskedasticity in Non-Gaussian Time Series (August 20, 2004). EFMA 2004 Basel Meetings Paper. Available at SSRN: https://ssrn.com/abstract=493263 or http://dx.doi.org/10.2139/ssrn.493263

Burkhard Raunig (Contact Author)

Austrian National Bank - Economic Studies Division ( email )

POB 61
Vienna, A-1011
Austria
+43 1 404 20 7219 (Phone)
+43 1 404 20 7299 (Fax)

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