Using monthly returns to model conditional heteroscedasticity

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Abstract:

This empirical study examines the extent of non-linearity in a multivariate model of monthly financial series. To capture the conditional heteroscedasticity in the series, both the GARCH(1,1) and GARCH(1,1)-in-mean models are employed. The conditional errors are assumed to follow the normal and Student-t distributions. The non-linearity in the residuals of a standard OLS regression are also assessed. It is found that the OLS residuals as well as conditional errors of the GARCH models exhibit strong non-linearity. Under the Student density, the extent of non-linearity in the GARCH conditional errors was generally similar to those of the standard OLS. The GARCH-in-mean regression generated the worse out-of-sample forecasts.

Document Type: Research Article

DOI: http://dx.doi.org/10.1080/0003684021000088536

Affiliations: Aston Business School, Aston University, Aston Triangle, Birmingham B4 7ET E-mail: n.l.joseph@aston.ac.uk

Publication date: May 1, 2003

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