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Using monthly returns to model conditional heteroscedasticity

  • Nathan Lael Joseph
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    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.

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    Article provided by Taylor & Francis Journals in its journal Applied Economics.

    Volume (Year): 35 (2003)
    Issue (Month): 7 ()
    Pages: 791-801

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    Handle: RePEc:taf:applec:v:35:y:2003:i:7:p:791-801
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